Evaluating the impact of proactivity in the user experience of a context-aware restaurant recommender for Android smartphones

Proactivity has recently gained much attention in the area of cyber physical systems as sensing the users’ real world environment is needed to achieve it. Proactive recommenders are a good example because they push recommendations to the user when the current situation seems appropriate, without explicit user request. Evaluating whether users would accept proactive recommendations, how to properly notify them and how to present recommended items are important research questions. In this article, we present the scenario related to a context-aware restaurant recommender for Android smartphones. Two options to achieve proactivity have been designed: a widget- and a notification-based solution. In addition, our mobile user interface includes a visualization of recommended items and allows for user feedback. The approach was evaluated in a survey among users with good results regarding usefulness and effectiveness. The results also showed that test users preferred the widget-based solution in terms of user experience.     

Progress in context-aware recommender systems — An overview

Recommender Systems are the set of tools and techniques to provide useful recommendations and suggestions to the users to help them in the decision-making process for choosing the right products or services. The recommender systems tailored to leverage contextual information (such as location, time, companion or such) in the recommendation process are called context-aware recommender systems. This paper presents a review on the continual development of context-aware recommender systems by analyzing different kinds of contexts without limiting to any specific application domain. First, an in-depth analysis is conducted on different recommendation algorithms used in context-aware recommender systems. Then this information is used to find out that how these techniques deals with the curse of dimensionality, which is an inherent issue in such systems. Since contexts are primarily based on users’ activity patterns that leads to the development of personalized recommendation services for the users. Thus, this paper also presents a review on how this contextual information is represented (either explicitly or implicitly) in the recommendation process. We also presented a list of datasets and evaluation metrics used in the setting of CARS. We tried to highlight that how algorithmic approaches used in CARS differ from those of conventional RS. In that, we presented what modification or additions are being applied on the top of conventional recommendation approaches to produce context-aware recommendations. Finally, the outstanding challenges and research opportunities are presented in front of the research community for analysis     

基于检测响应的安全协同推荐系统研究

协同推荐系统广泛地应用于电子商务和信息访问系统,为新用户提供个性化的产品建议。然而,协同推荐系统存在着严重的安全隐患,使得恶意用户能够注入伪造的描述文件,影响或破坏提供给其他用户的推荐建议。本文探讨了检测响应描述文件注入攻击的方法,改进了协同过滤推荐算法,设计了基于检测响应方法的安全协同推荐系统框架。     

基于上下文感知的普适服务框架

为了更好地适应用户的个性化需求和普适计算环境的特征,提出一种基于上下文感知的普适服务框架。该框架包括服务呈现层、服务管理层、服务提供层和上下文感知层。通过基于社区的服务管理方式来屏蔽普适计算环境中服务的异构性、分布性,动态地感知与当前计算环境和用户活动相关的上下文信息,采用基于上下文的服务推荐机制为用户提供个性化的服务,从而更好地适应人的意图和环境 因素。     

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.     

Hybreed: A software framework for developing context-aware hybrid recommender systems

This article introduces Hybreed, a software framework for building complex context-aware applications, together with a set of components that are specifically targeted at developing hybrid, context-aware recommender systems. Hybreed is based on a concept for processing context that we call dynamic contextualization. The underlying notion of context is very generic, enabling application developers to exploit sensor-based physical factors as well as factors derived from user models or user interaction. This approach is well aligned with context definitions that emphasize the dynamic and activity-oriented nature of context. As an extension of the generic framework, we describe Hybreed RecViews, a set of components facilitating the development of context-aware and hybrid recommender systems. With Hybreed and RecViews, developers can rapidly develop context-aware applications that generate recommendations for both individual users and groups. The framework provides a range of recommendation algorithms and strategies for producing group recommendations as well as templates for combining different methods into hybrid recommenders. Hybreed also provides means for integrating existing user or product data from external sources such as social networks. It combines aspects known from context processing frameworks with features of state-of-the-art recommender system frameworks, aspects that have been addressed only separately in previous research. To our knowledge, Hybreed is the first framework to cover all these aspects in an integrated manner. To evaluate the framework and its conceptual foundation, we verified its capabilities in three different use cases. The evaluation also comprises a comparative assessment of Hybreed’s functional features, a comparison to existing frameworks, and a user study assessing its usability for developers. The results of this study indicate that Hybreed is intuitive to use and extend by developers.     

A user-centric evaluation of context-aware recommendations for a mobile news service

Traditional recommender systems provide personal suggestions based on the user’s preferences, without taking into account any additional contextual information, such as time or device type. The added value of contextual information for the recommendation process is highly dependent on the application domain, the type of contextual information, and variations in users’ usage behavior in different contextual situations. This paper investigates whether users utilize a mobile news service in different contextual situations and whether the context has an influence on their consumption behavior. Furthermore, the importance of context for the recommendation process is investigated by comparing the user satisfaction with recommendations based on an explicit static profile, content-based recommendations using the actual user behavior but ignoring the context, and context-aware content-based recommendations incorporating user behavior as well as context. Considering the recommendations based on the static profile as a reference condition, the results indicate a significant improvement for recommendations that are based on the actual user behavior. This improvement is due to the discrepancy between explicitly stated preferences (initial profile) and the actual consumption behavior of the user. The context-aware content-based recommendations did not significantly outperform the content-based recommendations in our user study. Context-aware content-based recommendations may induce a higher user satisfaction after a longer period of service operation, enabling the recommender to overcome the cold-start problem and distinguish user preferences in various contextual situations.     

Utilizing contextual ontological user profiles for personalized recommendations

As users may have different needs in different situations and contexts, it is increasingly important to consider user context data when filtering information. In the field of web personalization and recommender systems, most of the studies have focused on the process of modelling user profiles and the personalization process in order to provide personalized services to the user, but not on contextualized services. Rather limited attention has been paid to investigate how to discover, model, exploit and integrate context information in personalization systems in a generic way. In this paper, we aim at providing a novel model to build, exploit and integrate context information with a web personalization system. A context-aware personalization system (CAPS) is developed which is able to model and build contextual and personalized ontological user profiles based on the user’s interests and context information. These profiles are then exploited in order to infer and provide contextual recommendations to users. The methods and system developed are evaluated through a user study which shows that considering context information in web personalization systems can provide more effective personalization services and offer better recommendations to users.     

Improving neighborhood based Collaborative Filtering via integrated folksonomy information

Personalized recommender systems which can provide people with suggestions according to individual interests usually rely on Collaborative Filtering (CF). The neighborhood based model (NBM) is a common choice when implementing such recommenders due to the intuitive nature; however, the recommendation accuracy is a major concern. Current NBM based recommenders mostly address the accuracy issue based on the rating data alone, whereas research on hybrid recommender systems suggests that users enjoy specifying feedback about items across multiple dimensions. In this work we aim to improve the accuracy of NBM via integrating the folksonomy information. To achieve this objective, we first propose the folksonomy network (FN) to analyze the item relevance described by the folksonomy data. We subsequently integrate the obtained folksonomy information into the global-optimization based NBM for making multi-source based recommendations. Experiments on the MovieLens dataset suggest positive results, which prove the efficiency of our strategy.     

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.     

Context-aware recommender systems in mobile environment: On the road of future research

Recommender systems have recently been singled out as a fascinating area of research, owing to the technological progress in mobile devices, such as smartphones and tablets, as well as to the rapid growth of social networking. In this respect, the main purpose of recommender systems is to suggest items that help users to make decisions from a large number of possible actions such as what place to visit, what movie to watch, or which friend to add to a social network system. In mobile environment, many personal, social and environmental contextual factors can be integrated into the recommendation process in order to provide the correct recommendation to a special user, at the perfect moment, in the appropriate location based on his/her emotional state, his/her current activity and past behavior. This paper provides an overview of context-aware recommender systems in mobile environment. The objective of this systematic review is to investigate the current state of the art in context-aware recommender systems and classify the reviewed research papers. This study aims equally to identify the possible future directions in this research area.     

Personalized blog content recommender system for mobile phone users

Compared to newspaper columnists and broadcast media commentators, bloggers do not have organizations actively promoting their content to users; instead, they rely on word-of-mouth or casual visits by web surfers. We believe the WAP Push service feature of mobile phones can help bridge the gap between internet and mobile services, and expand the number of potential blog readers. Since mobile phone screen size is very limited, content providers must be familiar with individual user preferences in order to recommend content that matches narrowly defined personal interests. To help identify popular blog topics, we have created (a) an information retrieval process that clusters blogs into groups based on keyword analyses, and (b) a mobile content recommender system (M-CRS) for calculating user preferences for new blog documents. Here we describe results from a case study involving 20,000 mobile phone users in which we examined the effects of personalized content recommendations. Browsing habits and user histories were recorded and analyzed to determine individual preferences for making content recommendations via the WAP Push feature. The evaluation results of our recommender system indicate significant increases in both blog-related push service click rates and user time spent reading personalized web pages. The process used in this study supports accurate recommendations of personalized mobile content according to user interests. This approach can be applied to other embedded systems with device limitations, since document subject lines are elaborated and more attractive to intended users.     

Personalized recommender system based on friendship strength in social network services

The rapid growth of social network services has produced a considerable amount of data, called big social data. Big social data are helpful for improving personalized recommender systems because these enormous data have various characteristics. Therefore, many personalized recommender systems based on big social data have been proposed, in particular models that use people relationship information. However, most existing studies have provided recommendations on special purpose and single-domain SNS that have a set of users with similar tastes, such as MovieLens and Last.fm; nonetheless, they have considered closeness relation. In this paper, we introduce an appropriate measure to calculate the closeness between users in a social circle, namely, the friendship strength. Further, we propose a friendship strength-based personalized recommender system that recommends topics or interests users might have in order to analyze big social data, using Twitter in particular. The proposed measure provides precise recommendations in multi-domain environments that have various topics. We evaluated the proposed system using one month's Twitter data based on various evaluation metrics. Our experimental results show that our personalized recommender system outperforms the baseline systems, and friendship strength is of great importance in personalized recommendation.     

Affective recommender systems in online news industry: how emotions influence reading choices

Recommender systems have become ubiquitous over the last decade, providing users with personalized search results, video streams, news excerpts, and purchasing hints. Human emotions are widely regarded as important predictors of behavior and preference. They are a crucial factor in decision making, but until recently, relatively little has been known about the effectiveness of using human emotions in personalizing real-world recommender systems. In this paper we introduce the Emotion Aware Recommender System (EARS), a large scale system for recommending news items using user’s self-assessed emotional reactions. Our original contribution includes the formulation of a multi-dimensional model of emotions for news item recommendations, introduction of affective item features that can be used to describe recommended items, construction of affective similarity measures, and validation of the EARS on a large corpus of real-world Web traffic. We collect over 13,000,000 page views from 2,700,000 unique users of two news sites and we gather over 160,000 emotional reactions to 85,000 news articles. We discover that incorporating pleasant emotions into collaborative filtering recommendations consistently outperforms all other algorithms. We also find that targeting recommendations by selected emotional reactions presents a promising direction for further research. As an additional contribution we share our experiences in designing and developing a real-world emotion-based recommendation engine, pointing to various challenges posed by the practical aspects of deploying emotion-based recommenders.

     

Applications of wavelet data reduction in a recommender system

Memory-based collaborative filtering (CF) recommender systems have emerged as an effective technique for information filtering. CF recommenders are being widely adopted for e-commerce applications to assist users in finding and selecting items of interest. As a result, the scalability of CF recommenders presents a significant challenge; one that is particularly resilient because the volume of data these systems utilize will continue to increase over time. This paper examines the impact of discrete wavelet transformation (DWT) as an approach to enhance the scalability of memory-based collaborative filtering recommender systems. In particular, a wavelet transformation methodology is proposed and applied to both synthetic and real-world recommender ratings. For experimental purposes, the DWT methodology’s effect on predictive accuracy and calculation speed is evaluated to compare recommendation quality and performance.     

A Deep Learning Based Approach for Context-Aware Multi-Criteria Recommender Systems

Recommender systems are similar to an information filtering system that helps identify items that best satisfy the users’ demands based on their preference profiles. Context-aware recommender systems (CARSs) and multi-criteria recommender systems (MCRSs) are extensions of traditional recommender systems. CARSs have integrated additional contextual information such as time, place, and so on for providing better recommendations. However, the majority of CARSs use ratings as a unique criterion for building communities. Meanwhile, MCRSs utilize user preferences in multiple criteria to better generate recommendations. Up to now, how to exploit context in MCRSs is still an open issue. This paper proposes a novel approach, which relies on deep learning for context-aware multi-criteria recommender systems. We apply deep neural network (DNN) models to predict the context-aware multi-criteria ratings and learn the aggregation function. We conduct experiments to evaluate the effect of this approach on the real-world dataset. A significant result is that our method outperforms other state-of-the-art methods for recommendation effectiveness.     

Socialized healthcare service recommendation using deep learning

Socialized recommender system recommends reliable healthcare services for users. Ratings are predicted on the healthcare services by merging recommendations given by users who has social relations with the active users. However, existing works did not consider the influence of distrust between users. They recommend items only based on the trust relations between users. We therefore propose a novel deep learning-based socialized healthcare service recommender model, which recommends healthcare services with recommendations given by recommenders with both trust relations and distrust relations with the active users. The influences of recommenders, considering both the node information and the structure information, are merged via the deep learning model. Experimental results show that the proposed model outperforms the existing works on prediction accuracy and prediction coverage simultaneously, even for cold start users or users with very sparse trust relations. It is also computational less expensive.

     

Collaborative user modeling for enhanced content filtering in recommender systems

Recommender systems, which have emerged in response to the problem of information overload, provide users with recommendations of content suited to their needs. To provide proper recommendations to users, personalized recommender systems require accurate user models of characteristics, preferences and needs. In this study, we propose a collaborative approach to user modeling for enhancing personalized recommendations to users. Our approach first discovers useful and meaningful user patterns, and then enriches the personal model with collaboration from other similar users. In order to evaluate the performance of our approach, we compare experimental results with those of a probabilistic learning model, a user model based on collaborative filtering approaches, and a vector space model. We present experimental results that show how our model performs better than existing alternatives.     

A web-based pervasive recommendation system for mobile tourist guides

Mobile tourist guides have attracted considerable research interest during the past decade, resulting in numerous standalone and web-based mobile applications. Particular emphasis has been given to personalization of services, typically based on travel recommender systems used to assist tourists in choosing places to visit; these systems address an important aspect of personalization and hence reduce the information burden for the user. However, existing systems fail to exploit information, behaviours, evaluations or ratings of other tourists with similar interests, which would potentially provide ground for the cooperative production of improved tourist content and travel recommendations. In this paper, we extend this notion of travel recommender systems utilizing collaborative filtering techniques while also taking into account contextual information (such as the current user’s location, time, weather conditions and places already visited by the user) for deriving improved recommendations in pervasive environments. We also propose the use of wireless sensor network (WSN) installations around tourist sites for enabling precise localization and also providing mobile users convenient and inexpensive means for uploading tourist information and ratings about points of interest (POI) via their mobile devices. We also introduce the concept of ‘context-aware rating’, whereby user ratings uploaded through WSN infrastructures are weighted higher to differentiate among users that rate POIs using the mobile tourist guide application while onsite and others using the Internet away from the POI.