A stochastic approach to group recommendations in social media systems

Recommender systems are becoming increasingly important not only to individual users but also to groups of people. This study focuses on the issue of recommending items to communities of interest (i.e., groups) that are specifically formed in social media systems. To deal with this issue, we introduce a new graph model that profits from fruitful tagging information. By using the proposed graph model, we present a stochastic method that makes recommendations based on link-structure analysis in a probabilistic manner. This method supports two ways of computing group ranking scores for items—via a preference aggregation approach and via a ranking aggregation approach, but ensures the same ranking results. We also explore the influence of users and items associated with a group in the facilitation of more accurate recommendations. Our empirical evaluations with the Last.fm dataset corroborate the benefits of our graph model on group recommendations, and demonstrate that the proposed group recommendation method performs better than existing alternatives.     

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.     

Entertainment recommender systems for group of users

Recommender systems are used to recommend potentially interesting items to users in different domains. Nowadays, there is a wide range of domains in which there is a need to offer recommendations to group of users instead of individual users. As a consequence, there is also a need to address the preferences of individual members of a group of users so as to provide suggestions for groups as a whole. Group recommender systems present a whole set of new challenges within the field of recommender systems. In this article, we present two expert recommender systems that suggest entertainment to groups of users. These systems, jMusicGroupRecommender and jMoviesGroupRecommender, suggest music and movies and utilize different methods for the generation of group recommendations: merging recommendations made for individuals, aggregation of individuals’ ratings, and construction of group preference models. We also describe the results obtained when comparing different group recommendation techniques in both domains.     

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.     

基于马尔可夫聚类和混合协同过滤的电视节目推荐

针对家庭用户的电视节目个性化推荐问题,提出一种基于马尔可夫聚类和混合协同过滤(MCL-HCF)算法的混合推荐方法。采用马尔可夫聚类对各个时间段的电视用户进行聚类,产生不同的群组,最小化每个群组里的个体成员和群组整体的偏好差异,再以群组为单位进行电视节目推荐;使用基于物品的协同过滤和基于用户的协同过滤算法分别产生推荐列表;采用基于加权融合的混合推荐算法对两个推荐列表进行处理,得到最终的混合推荐结果。在公开数据集上的实验结果表明,该算法在平衡推荐惊喜度和相关性的同时能够获得令人满意的推荐准确率。     

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.     

融合循环知识图谱和协同过滤电影推荐算法

推荐系统对筛选有效信息和提高信息获取效率具有重大的意义。传统的推荐系统会面临数据稀松和冷启动等问题。利用外部评分和物品内涵知识相结合,提出一种基于循环知识图谱和协同过滤的电影推荐模型--RKGE-CF。在充分考虑物品、用户、评分之间的相关性后,利用基于物品和用户的协同过滤进行Top-[K]推荐;将物品的外部附加数据和用户偏好数据加入知识图谱,提取实体相互之间的依赖关系,构建用户和物品之间的交互信息,以便揭示实体与关系之间的语义,帮助理解用户兴趣;将多种推荐结果按不同方法融合进行对比;模型训练时使用多组不同的负样本作为对比,以优化模型;最后利用真实电影数Movielens和IMDB映射连接成新数据集进行测试。实验结果证明该模型对于推荐效果的准确率有显著的提升,同时能更好地解释推荐背后的原因。     

A context-aware recommender method based on text and opinion mining

A recommender system is an information filtering technology that can be used to recommend items that may be of interest to users. Additionally, there are the context-aware recommender systems that consider contextual information to generate the recommendations. Reviews can provide relevant information that can be used by recommender systems, including contextual and opinion information. In a previous work, we proposed a context-aware recommendation method based on text mining (CARM-TM). The method includes two techniques to extract context from reviews: CIET.5_embed, a technique based on word embeddings; and RulesContext, a technique based on association rules. In this work, we have extended our previous method by including CEOM, a new technique which extracts context by using aspect-based opinions. We call our extension of CARM-TOM (context-aware recommendation method based on text and opinion mining). To generate recommendations, our method makes use of the CAMF algorithm, a context-aware recommender based on matrix factorization. To evaluate CARM-TOM, we ran an extensive set of experiments in a dataset about restaurants, comparing CARM-TOM against the MF algorithm, an uncontextual recommender system based on matrix factorization; and against a context extraction method proposed in literature. The empirical results strongly indicate that our method is able to improve a context-aware recommender system.     

A framework for tag-aware recommender systems

In social tagging system, a user annotates a tag to an item. The tagging information is utilized in recommendation process. In this paper, we propose a hybrid item recommendation method to mitigate limitations of existing approaches and propose a recommendation framework for social tagging systems. The proposed framework consists of tag and item recommendations. Tag recommendation helps users annotate tags and enriches the dataset of a social tagging system. Item recommendation utilizes tags to recommend relevant items to users. We investigate association rule, bigram, tag expansion, and implicit trust relationship for providing tag and item recommendations on the framework. The experimental results show that the proposed hybrid item recommendation method generates more appropriate items than existing research studies on a real-world social tagging dataset.     

A flexible semantic inference methodology to reason about user preferences in knowledge-based recommender systems

Recommender systems arose with the goal of helping users search in overloaded information domains (like e-commerce, e-learning or Digital TV). These tools automatically select items (commercial products, educational courses, TV programs, etc.) that may be appealing to each user taking into account his/her personal preferences. The personalization strategies used to compare these preferences with the available items suffer from well-known deficiencies that reduce the quality of the recommendations. Most of the limitations arise from using syntactic matching techniques because they miss a lot of useful knowledge during the recommendation process. In this paper, we propose a personalization strategy that overcomes these drawbacks by applying inference techniques borrowed from the Semantic Web. Our approach reasons about the semantics of items and user preferences to discover complex associations between them. These semantic associations provide additional knowledge about the user preferences, and permit the recommender system to compare them with the available items in a more effective way. The proposed strategy is flexible enough to be applied in many recommender systems, regardless of their application domain. Here, we illustrate its use in AVATAR, a tool that selects appealing audiovisual programs from among the myriad available in Digital TV.     

Recommending packages with validity constraints to groups of users

The success of recommender systems has made them the focus of a massive research effort in both industry and academia. Recent work has generalized recommendations to suggest packages of items to single users, or single items to groups of users. However, to the best of our knowledge, the interesting problem of recommending a package to a group of users (P2G) has not been studied to date. This is a problem with several practical applications, such as recommending vacation packages to tourist groups, entertainment packages to groups of friends or sets of courses to groups of students. In this paper, we formulate the P2G problem, and we propose probabilistic models that capture the preference of a group toward a package, incorporating factors such as user impact and package viability. We also investigate the issue of recommendation fairness. This is a novel consideration that arises in our setting, where we require that no user is consistently slighted by the item selection in the package. In addition, we study a special case of the P2G problem, where the recommended items are places and the recommendation should consider the current locations of the users in the group. We present aggregation algorithms for finding the best packages and compare our suggested models with baseline approaches stemming from previous work. The results show that our models find packages of high quality which consider all special requirements of P2G recommendation.     

Interactively Co-segmentating Topically Related Images with Intelligent Scribble Guidance

We present an algorithm for Interactive Co-segmentation of a foreground object from a group of related images. While previous works in co-segmentation have focussed on unsupervised co-segmentation, we use successful ideas from the interactive object-cutout literature. We develop an algorithm that allows users to decide what foreground is, and then guide the output of the co-segmentation algorithm towards it via scribbles. Interestingly, keeping a user in the loop leads to simpler and highly parallelizable energy functions, allowing us to work with significantly more images per group. However, unlike the interactive single-image counterpart, a user cannot be expected to exhaustively examine all cutouts (from tens of images) returned by the system to make corrections. Hence, we propose iCoseg, an automatic recommendation system that intelligently recommends where the user should scribble next. We introduce and make publicly available the largest co-segmentation dataset yet, the CMU-Cornell iCoseg dataset, with 38 groups, 643 images, and pixelwise hand-annotated groundtruth. Through machine experiments and real user studies with our developed interface, we show that iCoseg can intelligently recommend regions to scribble on, and users following these recommendations can achieve good quality cutouts with significantly lower time and effort than exhaustively examining all cutouts.     

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。     

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.     

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.      

Privileged contextual information for context-aware recommender systems

A recommender system is used in various fields to recommend items of interest to the users. Most recommender approaches focus only on the users and items to make the recommendations. However, in many applications, it is also important to incorporate contextual information into the recommendation process. Although the use of contextual information has received great focus in recent years, there is a lack of automatic methods to obtain such information for context-aware recommender systems. Some works address this problem by proposing supervised methods, which require greater human effort and whose results are not so satisfactory. In this scenario, we propose an unsupervised method to extract contextual information from web page content. Our method builds topic hierarchies from page textual content considering, besides the traditional bag-of-words, valuable information of texts as named entities and domain terms (privileged information). The topics extracted from the hierarchies are used as contextual information in context-aware recommender systems. We conducted experiments by using two data sets and two baselines: the first baseline is a recommendation system that does not use contextual information and the second baseline is a method proposed in literature to extract contextual information. The results are, in general, very good and present significant gains. In conclusion, our method has advantages and innovations:(i) it is unsupervised; (ii) it considers the context of the item (Web page), instead of the context of the user as in most of the few existing methods, which is an innovation; (iii) it uses privileged information in addition to the existing technical information from pages; and (iv) it presented good and promising empirical results. This work represents an advance in the state-of-the-art in context extraction, which means an important contribution to context-aware recommender systems, a kind of specialized and intelligent system.     

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.     

CAESAR: context-aware explanation based on supervised attention for service recommendations

Explainable recommendations have drawn more attention from both academia and industry recently, because they can help users better understand recommendations (i.e., why some particular items are recommended), therefore improving the persuasiveness of the recommender system and users’ satisfaction. However, little work has been done to provide explanations from the angle of a user’s contextual situations (e.g., companion, season, and destination if the recommendation is a hotel). To fill this research gap, we propose a new context-aware recommendation algorithm based on supervised attention mechanism (CAESAR), which particularly matches latent features to explicit contextual features as mined from user-generated reviews for producing context-aware explanations. Experimental results on two large datasets in hotel and restaurant service domains demonstrate that our model improves recommendation performance against the state-of-the-art methods and furthermore is able to return feature-level explanations that can adapt to the target user’s current contexts.

     

Neural TV program recommendation with label and user dual attention

TV program recommendation is very important for users to find interesting TV programs and avoid confusing users with a lot of information. Currently, they are basically traditional collaborative filtering algorithms, which only recommend through the interactive data between users and programs ignoring the important value of some auxiliary information. In addition, the neural network method based on attention mechanism can well capture the relationship between program labels to obtain accurate program and user representations. In this paper, we propose a neural TV program recommendation with label and user dual attention (NPR-LUA), which can focus on auxiliary information in program and user modules. In the program encoder module, we learn the auxiliary information from program labels through neural networks and word attention to identify important program labels. In the user encoder module, we learn the user representation through the programs that the user watches and use personalized attention mechanism to distinguish the importance of programs for each user. Experiments on real data sets show that our method can effectively improve the effectiveness of TV program recommendations than other existing models.