Social group recommendation in the tourism domain

Recommender Systems learn users’ preferences and tastes in different domains to suggest potentially interesting items to users. Group Recommender Systems generate recommendations that intend to satisfy a group of users as a whole, instead of individual users. In this article, we present a social based approach for recommender systems in the tourism domain, which builds a group profile by analyzing not only users’ preferences, but also the social relationships between members of a group. This aspect is a hot research topic in the recommender systems area. In addition, to generate the individual and group recommendations our approach uses a hybrid technique that combines three well-known filtering techniques: collaborative, content-based and demographic filtering. In this way, the disadvantages of one technique are overcome by the others. Our approach was materialized in a recommender system named Hermes, which suggests tourist attractions to both individuals and groups of users. We have obtained promising results when comparing our approach with classic approaches to generate recommendations to individual users and groups. These results suggest that considering the type of users’ relationship to provide recommendations to groups leads to more accurate recommendations in the tourism domain. These findings can be helpful for recommender systems developers and for researchers in this area.     

Folkommender: a group recommender system based on a graph-based ranking algorithm

With the rapid popularity of smart devices, users are easily and conveniently accessing rich multimedia content. Consequentially, the increasing need for recommender services, from both individual users and groups of users, has arisen. In this paper, we present a new graph-based approach to a recommender system, called Folkommender, that can make recommendations most notably to groups of users. From rating information, we first model a signed graph that contains both positive and negative links between users and items. On this graph we examine two distinct random walks to separately quantify the degree to which a group of users would like or dislike items. We then employ a differential ranking approach for tailoring recommendations to the group. Our empirical evaluations on two real-world datasets demonstrate that the proposed group recommendation method performs better than existing alternatives. We also demonstrate the feasibility of Folkommender for smartphones.     

An overview of recommender systems in the healthy food domain

Recently, food recommender systems have received increasing attention due to their relevance for healthy living. Most existing studies on the food domain focus on recommendations that suggest proper food items for individual users on the basis of considering their preferences or health problems. These systems also provide functionalities to keep track of nutritional consumption as well as to persuade users to change their eating behavior in positive ways. Also, group recommendation functionalities are very useful in the food domain, especially when a group of users wants to have a dinner together at home or have a birthday party in a restaurant. Such scenarios create many challenges for food recommender systems since the preferences of all group members have to be taken into account in an adequate fashion. In this paper, we present an overview of recommendation techniques for individuals and groups in the healthy food domain. In addition, we analyze the existing state-of-the-art in food recommender systems and discuss research challenges related to the development of future food recommendation technologies.     

Efficiently using contextual influence to recommend new items to ephemeral groups

Group recommender systems suggest items to groups of users that want to utilize those items together. These systems can support several activities that can be performed together with other people and are typically social, like watching TV or going to the restaurant. In this paper we study ephemeral groups, i.e., groups constituted by users who are together for the first time, and for which therefore there is no history of past group activities.Recent works have studied ephemeral group recommendations proposing techniques that learn complex models of users and items. These techniques, however, are not appropriate to recommend items that are new in the system, while we propose a method able to deal with new items too. Specifically, our technique determines the preference of a group for a given item by combining the individual preferences of the group members on the basis of their contextual influence, the contextual influence representing the ability of an individual, in a given situation, to guide the group’s decision. Moreover, while many works on recommendations do not consider the problem of efficiently producing recommendation lists at runtime, in this paper we speed up the recommendation process by applying techniques conceived for the top-$K$ query processing problem. Finally, we present extensive experiments, evaluating: (i) the accuracy of the recommendations, using a real TV dataset containing a log of viewings performed by real groups, and (ii) the efficiency of the online recommendation task, exploiting also a bigger partially synthetic dataset.     

Recommending people to people: the nature of reciprocal recommenders with a case study in online dating

People-to-people recommenders constitute an important class of recommender systems. Examples include online dating, where people have the common goal of finding a partner, and employment websites where one group of users needs to find a job (employer) and another group needs to find an employee. People-to-people recommenders differ from the traditional items-to-people recommenders as they must satisfy both parties; we call this type of recommender reciprocal. This article is the first to present a comprehensive view of this important recommender class. We first identify the characteristics of reciprocal recommenders and compare them with traditional recommenders, which are widely used in e-commerce websites. We then present a series of studies and evaluations of a content-based reciprocal recommender in the domain of online dating. It uses a large dataset from a major online dating website. We use this case study to illustrate the distinctive requirements of reciprocal recommenders and highlight important challenges, such as the need to avoid bad recommendations since they may make users to feel rejected. Our experiments indicate that, by considering reciprocity, the rate of successful connections can be significantly improved. They also show that, despite the existence of rich explicit profiles, the use of implicit profiles provides more effective recommendations. We conclude with a discussion, linking our work in online dating to the many other domains that require reciprocal recommenders. Our key contributions are the recognition of the reciprocal recommender as an important class of recommender, the identification of its distinctive characteristics and the exploration of how these impact the recommendation process in an extensive case study in the domain of online dating.     

Generating recommendations for consensus negotiation in group personalization services

There are increasingly many personalization services in ubiquitous computing environments that involve a group of users rather than individuals. Ubiquitous commerce is one example of these environments. Ubiquitous commerce research is highly related to recommender systems that have the ability to provide even the most tentative shoppers with compelling and timely item suggestions. When the recommendations are made for a group of users, new challenges and issues arise to provide compelling item suggestions. One of the challenges a group recommender system must cope with is the potentially conflicting preferences of multiple users when selecting items for recommendation. In this paper, we focus on how individual user models can be aggregated to reach a consensus on recommendations. We describe and evaluate nine different consensus strategies and analyze them to highlight the benefits of group recommendation using live-user preference data. Moreover, we show that the performance is significantly different among strategies.     

基于用户偏好融合的组推荐算法综述

传统的推荐系统是面向单个用户的推荐。作为个性化推荐的一个新的延伸,目前有越来越多的推荐系统正试图面向一组成员进行推荐。将推荐对象从单个用户扩展到一组用户的转变带来了许多新的课题,该文将主要介绍目前已有的几种组推荐算法,并总结一般组推荐系统的偏好融合过程。     

Comparison of group recommendation algorithms

In recent years recommender systems have become the common tool to handle the information overload problem of educational and informative web sites, content delivery systems, and online shops. Although most recommender systems make suggestions for individual users, in many circumstances the selected items (e.g., movies) are not intended for personal usage but rather for consumption in groups. This paper investigates how effective group recommendations for movies can be generated by combining the group members’ preferences (as expressed by ratings) or by combining the group members’ recommendations. These two grouping strategies, which convert traditional recommendation algorithms into group recommendation algorithms, are combined with five commonly used recommendation algorithms to calculate group recommendations for different group compositions. The group recommendations are not only assessed in terms of accuracy, but also in terms of other qualitative aspects that are important for users such as diversity, coverage, and serendipity. In addition, the paper discusses the influence of the size and composition of the group on the quality of the recommendations. The results show that the grouping strategy which produces the most accurate results depends on the algorithm that is used for generating individual recommendations. Therefore, the paper proposes a combination of grouping strategies which outperforms each individual strategy in terms of accuracy. Besides, the results show that the accuracy of the group recommendations increases as the similarity between members of the group increases. Also the diversity, coverage, and serendipity of the group recommendations are to a large extent dependent on the used grouping strategy and recommendation algorithm. Consequently for (commercial) group recommender systems, the grouping strategy and algorithm have to be chosen carefully in order to optimize the desired quality metrics of the group recommendations. The conclusions of this paper can be used as guidelines for this selection process.     

Discovery and representation of the preferences of automatically detected groups: Exploiting the link between group modeling and clustering

There are types of information systems, like those that produce group recommendations or a market segmentation, in which it is necessary to aggregate big amounts of data about a group of users in order to filter the data. Group modeling is the process that combines multiple user models into a single model that represents the knowledge available about the preferences of the users in a group. In group recommendation, group modeling allows a system to derive a group preference for each item. Different strategies lead to completely different group models, so the strategy used to model a group has to be evaluated in the domain in which the group recommender system operates. This paper evaluates group modeling strategies in a group recommendation scenario in which groups are detected by clustering the users. Once users are clustered and groups are formed, different strategies are tested, in order to find the one that allows a group recommender system to get the best accuracy. Experimental results show that the strategy used to build the group models strongly affects the performance of a group recommender system. An interesting property derived by our study is that clustering and group modeling are strongly connected. Indeed, the modeling strategy takes the same role that the centroid has when users are clustered, by producing group preferences that are equally distant from the preferences of every user. This “continuity” among the two tasks is essential in order to build accurate group recommendations.     

Composite recommendations: from items to packages

Classical recommender systems provide users with a list of recommendations where each recommendation consists of a single item, e.g., a book or DVD. However, several applications can benefit from a system capable of recommending packages of items, in the form of sets. Sample applications include travel planning with a limited budget (price or time) and twitter users wanting to select worthwhile tweeters to follow, given that they can deal with only a bounded number of tweets. In these contexts, there is a need for a system that can recommend the top-k packages for the user to choose from. Motivated by these applications, we consider composite recommendations, where each recommendation comprises a set of items. Each item has both a value (rating) and a cost associated with it, and the user specifies a maximum total cost (budget) for any recommended set of items. Our composite recommender system has access to one or more component recommender systems focusing on different domains, as well as to information sources which can provide the cost associated with each item. Because the problem of decidingwhether there is a recommendation (package)whose cost is under a given budget and whose value exceeds some threshold is NP-complete, we devise several approximation algorithms for generating the top-k packages as recommendations. We analyze the efficiency as well as approximation quality of these algorithms. Finally, using two real and two synthetic datasets, we subject our algorithms to thorough experimentation and empirical analysis. Our findings attest to the efficiency and quality of our approximation algorithms for the top-k packages compared to exact algorithms.     

Private personalized social recommendations in an IPTV system

In our connected world, recommender systems have become widely known for their ability to provide expert and personalize referrals to end-users in different domains. The rapid growth of social networks and new kinds of systems so called “social recommender systems” are rising, where recommender systems can be utilized to find a suitable content according to end-users' personal preferences. However, preserving end-users' privacy in social recommender systems is a very challenging problem that might prevent end-users from releasing their own data, which detains the accuracy of extracted referrals. In order to gain accurate referrals, social recommender systems should have the ability to preserve the privacy of end-users registered in this system. In this paper, we present a middleware that runs on end-users' Set-top boxes to conceal their profile data when released for generating referrals, such that computation of recommendation proceeds over the concealed data. The proposed middleware is equipped with two concealment protocols to give users a complete control on the privacy level of their profiles. We present an IPTV network scenario and perform a number of different experiments to test the efficiency and accuracy of our protocols. As supported by the experiments, our protocols maintain the recommendations accuracy with acceptable privacy level.     

Critiquing-based recommenders: survey and emerging trends

Critiquing-based recommender systems elicit users?? feedback, called critiques, which they made on the recommended items. This conversational style of interaction is in contract to the standard model where users receive recommendations in a single interaction. Through the use of the critiquing feedback, the recommender systems are able to more accurately learn the users?? profiles, and therefore suggest better recommendations in the subsequent rounds. Critiquing-based recommenders have been widely studied in knowledge-, content-, and preference-based recommenders and are beginning to be tried in several online websites, such as MovieLens. This article examines the motivation and development of the subject area, and offers a detailed survey of the state of the art concerning the design of critiquing interfaces and development of algorithms for critiquing generation. With the help of categorization analysis, the survey reveals three principal branches of critiquing based recommender systems, using respectively natural language based, system-suggested, and user-initiated critiques. Representative example systems will be presented and analyzed for each branch, and their respective pros and cons will be discussed. Subsequently, a hybrid framework is developed to unify the advantages of different methods and overcome their respective limitations. Empirical findings from user studies are further presented, indicating how hybrid critiquing supports could effectively enable end-users to achieve more confident decisions. Finally, the article will point out several future trends to boost the advance of critiquing-based recommenders.     

基于用户信任和张量分解的社会网络推荐

社会化网络中的推荐系统可以在浩瀚的数据海洋中给用户推荐相关的信息。社会网络中用户之间的信任关系已经被用于推荐算法中,但是目前的基于信任的推荐算法都是单一的信任模型。提出了一种基于主题的张量分解的用户信任推荐算法,用来挖掘用户在不同的物品选取的时候对不同朋友的信任程度。由于社交网络更新速度快,鉴于目前的基于信任算法大都是静态算法,提出了一种增量更新的张量分解算法用于用户信任的推荐算法。实验结果表明：所提出的基于主题的用户信任推荐算法比现有算法具有更好的准确性,并且增量更新的推荐算法可以大幅度提高推荐算法在训练数据增加后的模型训练效率,适合更新速度快的社会化网络中的推荐任务。     

Incremental critiquing

Conversational recommender systems guide users through a product space, alternatively making concrete product suggestions and eliciting the user's feedback. Critiquing is a common form of user feedback, where users provide limited feedback at the feature-level by constraining a feature's value-space. For example, a user may request a cheaper product, thus critiquing the price feature. Usually, when critiquing is used in conversational recommender systems, there is little or no attempt to monitor successive critiques within a given recommendation session. In our experience this can lead to inefficiencies on the part of the recommender system, and confusion on the part of the user. In this paper we describe an approach to critiquing that attempts to consider a user's critiquing history, as well as their current critique, when making new recommendations. We provide experimental evidence to show that this has the potential to significantly improve recommendation efficiency.     

Providing Justifications in Recommender Systems

Recommender systems are gaining widespread acceptance in e-commerce applications to confront the “information overload” problem. Providing justification to a recommendation gives credibility to a recommender system. Some recommender systems (Amazon.com, etc.) try to explain their recommendations, in an effort to regain customer acceptance and trust. However, their explanations are not sufficient, because they are based solely on rating or navigational data, ignoring the content data. Several systems have proposed the combination of content data with rating data to provide more accurate recommendations, but they cannot provide qualitative justifications. In this paper, we propose a novel approach that attains both accurate and justifiable recommendations. We construct a feature profile for the users to reveal their favorite features. Moreover, we group users into biclusters (i.e., groups of users which exhibit highly correlated ratings on groups of items) to exploit partial matching between the preferences of the target user and each group of users. We have evaluated the quality of our justifications with an objective metric in two real data sets (Reuters and MovieLens), showing the superiority of the proposed method over existing approaches.      

Collaborative user modeling with user-generated tags for social recommender systems

With the popularity of social media services, the sheer amount of content is increasing exponentially on the Social Web that leads to attract considerable attention to recommender systems. Recommender systems provide users with recommendations of items suited to their needs. To provide proper recommendations to users, recommender systems require an accurate user model that can reflect a user’s characteristics, preferences and needs. In this study, by leveraging user-generated tags as preference indicators, we propose a new collaborative approach to user modeling that can be exploited to recommender systems. Our approach first discovers relevant and irrelevant topics for users, and then enriches an individual user model with collaboration from other similar users. In order to evaluate the performance of our model, we compare experimental results with a user model based on collaborative filtering approaches and a vector space model. The experimental results have shown the proposed model provides a better representation in user interests and achieves better recommendation results in terms of accuracy and ranking.     

Detection of the customer time-variant pattern for improving recommender systems

Due to the explosion of e-commerce, recommender systems are rapidly becoming a core tool to accelerate cross-selling and strengthen customer loyalty. There are two prevalent approaches for building recommender systems—content-based recommending and collaborative filtering. So far, collaborative filtering recommender systems have been very successful in both information filtering and e-commerce domains. However, the current research on recommendation has paid little attention to the use of time-related data in the recommendation process. Up to now there has not been any study on collaborative filtering to reflect changes in user interest.This paper suggests a methodology for detecting a user's time-variant pattern in order to improve the performance of collaborative filtering recommendations. The methodology consists of three phases of profiling, detecting changes, and recommendations. The proposed methodology detects changes in customer behavior using the customer data at different periods of time and improves the performance of recommendations using information on changes.     

Exploring synergies between content-based filtering and Spreading Activation techniques in knowledge-based recommender systems

Recommender systems fight information overload by selecting automatically items that match the personal preferences of each user. The so-called content-based recommenders suggest items similar to those the user liked in the past, using syntactic matching mechanisms. The rigid nature of such mechanisms leads to recommending only items that bear strong resemblance to those the user already knows. Traditional collaborative approaches face up to overspecialization by considering the preferences of other users, which causes other severe limitations. In this paper, we avoid the intrinsic pitfalls of collaborative solutions and diversify the recommendations by reasoning about the semantics of the user’s preferences. Specifically, we present a novel content-based recommendation strategy that resorts to semantic reasoning mechanisms adopted in the Semantic Web, such as Spreading Activation techniques and semantic associations. We have adopted these mechanisms to fulfill the personalization requirements of recommender systems, enabling to discover extra knowledge about the user’s preferences and leading to more accurate and diverse suggestions. Our approach is generic enough to be used in a wide variety of domains and recommender systems. The proposal has been preliminary evaluated by statistics-driven tests involving real users in the recommendation of Digital TV contents. The results reveal the users’ satisfaction regarding the accuracy and diversity of the reasoning-driven content-based recommendations.     

Evaluation and selection of group recommendation strategies for collaborative searching of learning objects

Nowadays, there is a wide variety of e-learning repositories that provide digital resources for education in the form of learning objects. Some of these systems provide recommender systems in order to help users in the search for and selection of the learning objects most appropriate to their individual needs. The search for and recommendation of learning objects are usually viewed as a solitary and individual task. However, a collaborative search can be more effective than an individual search in some situations – for example, when developing a digital course between a group of instructors. The problem of recommending learning objects to a group of users or instructors is much more difficult than the traditional problem of recommending to only one individual. To resolve this problem, this paper proposes a collaborative methodology for searching, selecting, rating and recommending learning objects. Additionally, voting aggregation strategies and meta-learning techniques are used in order to automatically obtain the final ratings without having to reach a consensus between all the instructors. A functional model has been implemented within the DELPHOS hybrid recommender system. Finally, various experiments have been carried out using 50 different groups in order to validate the proposed learning object group recommendation approach.     

Preference dynamics with multimodal user-item interactions in social media recommendation

Recommender systems elicit the interests and preferences of individuals and make recommendations accordingly, a main challenge for expert and intelligent systems. An essential problem in recommender systems is to learn users’ preference dynamics, that is, the constant evolution of the explicit or the implicit information, which is diversified throughout time according to the user actions. Also, in real settings data sparsity degrades the recommendation accuracy. Hence, state-of-the-art methods exploit multimodal information of users-item interactions to reduce sparsity, but they ignore preference dynamics and do not capture users’ most recent preferences. In this article, we present a Temporal Collective Matrix Factorization (TCMF) model, making the following contributions: (i) we capture preference dynamics through a joint decomposition model that extracts the user temporal patterns, and (ii) co-factorize the temporal patterns with multimodal user-item interactions by minimizing a joint objective function to generate the recommendations. We evaluate the performance of TCMF in terms of accuracy and root mean square error, and show that the proposed model significantly outperforms state-of-the-art strategies.