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146
Towards time-dependant recommendation based on implicit feedback
- In Workshop on context-aware recommender systems (CARSâ Ă´ Z09
, 2009
"... Context-aware recommender systems (CARS) aim at im-proving users ’ satisfaction by tailoring recommendations to each particular context. In this work we propose a con-textual pre-filtering technique based on implicit user feed-back. We introduce a new context-aware recommendation approach called use ..."
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Cited by 47 (3 self)
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Context-aware recommender systems (CARS) aim at im-proving users ’ satisfaction by tailoring recommendations to each particular context. In this work we propose a con-textual pre-filtering technique based on implicit user feed-back. We introduce a new context-aware recommendation approach called user micro-profiling. We split each single user profile into several possibly overlapping sub-profiles, each representing users in particular contexts. The predic-tions are done using these micro-profiles instead of a single user model. The users ’ taste can depend on the exact partition of the contextual variable. The identification of a meaningful par-tition of the users ’ profile and its evaluation is a non-trivial task, especially when using implicit feedback and a contin-uous contextual domain. We propose an off-line evaluation procedure for CARS in these conditions and evaluate our approach on a time-aware music recommendation sytem. 1.
Matrix factorization techniques for context aware recommendation
- In ACM RecSys
, 2011
"... Context aware recommender systems (CARS) adapt the recommendations to the specific situation in which the items will be consumed. In this paper we present a novel contextaware recommendation algorithm that extends Matrix Factorization. We model the interaction of the contextual factors with item rat ..."
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Cited by 30 (4 self)
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Context aware recommender systems (CARS) adapt the recommendations to the specific situation in which the items will be consumed. In this paper we present a novel contextaware recommendation algorithm that extends Matrix Factorization. We model the interaction of the contextual factors with item ratings introducing additional model parameters. The performed experiments show that the proposed solution provides comparable results to the best, state of the art, and more complex approaches. The proposed solution has the advantage of smaller computational cost and provides the possibility to represent at different granularities the interaction between context and items. We have exploited the proposed model in two recommendation applications: places of interest and music.
Group Recommender Systems: Combining individual models
"... Abstract This chapter shows how a system can recommend to a group of users by aggregating information from individual user models and modelling the users affective state. It summarizes results from previous research in this area. It also shows how group recommendation techniques can be applied when ..."
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Cited by 29 (0 self)
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Abstract This chapter shows how a system can recommend to a group of users by aggregating information from individual user models and modelling the users affective state. It summarizes results from previous research in this area. It also shows how group recommendation techniques can be applied when recommending to individuals, in particular for solving the cold-start problem and dealing with multiple criteria. 1
Contextaware places of interest recommendations for mobile users
- in Proc. of DUXU’11, ser. LNCS
, 2011
"... Abstract. Contextual knowledge has been traditionally used in Recommender Systems (RSs) to improve the recommendation accuracy of the core recommendation algorithm. Beyond this advantage, in this paper we argue that there is an additional benefit of context management; making more convincing recomme ..."
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Cited by 19 (2 self)
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Abstract. Contextual knowledge has been traditionally used in Recommender Systems (RSs) to improve the recommendation accuracy of the core recommendation algorithm. Beyond this advantage, in this paper we argue that there is an additional benefit of context management; making more convincing recommendations because the system can use the contextual situation of the user to explain why an item has been recommended, i.e., the RS can pinpoint the relationships between the contextual situation and the recommended items to justify the suggestions. The results of a user study indicate that context management and this type of explanations increase the user satisfaction with the recommender system. 1
Making Decisions about Privacy: Information Disclosure in Context-Aware Recommender Systems
, 2012
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Incarmusic: Context-aware music recommendations
- in acar.InE-Commerce and Web Technologies - 12th International Conference, EC-Web 2011
"... Abstract. Context aware recommender systems (CARS) adapt to the specific situation in which the recommended item will be consumed. So, for instance, music recommendations while the user is traveling by car should take into account the current traffic condition or the driver’s mood. This requires the ..."
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Cited by 17 (7 self)
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Abstract. Context aware recommender systems (CARS) adapt to the specific situation in which the recommended item will be consumed. So, for instance, music recommendations while the user is traveling by car should take into account the current traffic condition or the driver’s mood. This requires the acquisition of ratings for items in several alternative contextual situations, to extract from this data the true dependency of the ratings on the contextual situation. In this paper, in order to simplify the in-context rating acquisition process, we consider the individual perceptions of the users about the influence of context on their decisions. We have elaborated a system design methodology where we assume that users can be requested to judge: a) if a contextual factor (e.g., the traffic state) is relevant for their decision making task, and b) how they would rate an item assuming that a certain contextual condition (e.g., traffic is chaotic) holds. Using these evaluations we show that it is possible to build an effective context-aware mobile recommender system. 1
Differential context relaxation for context-aware travel recommendation
- In 13th International Conference on Electronic Commerce and Web Technologies (EC-WEB 2012
, 2012
"... Abstract. Context-aware recommendation (CARS) has been shown to be an effective approach to recommendation in a number of domains. However, the problem of identifying appropriate contextual variables remains: using too many contextual variables risks a drastic increase in dimensionality and a loss o ..."
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Cited by 13 (13 self)
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Abstract. Context-aware recommendation (CARS) has been shown to be an effective approach to recommendation in a number of domains. However, the problem of identifying appropriate contextual variables remains: using too many contextual variables risks a drastic increase in dimensionality and a loss of accu-racy in recommendation. In this paper, we propose a novel treatment of context – identifying influential contexts for different algorithm components instead of for the whole algorithm. Based on this idea, we take traditional user-based collabora-tive filtering (CF) as an example, decompose it into three context-sensitive com-ponents, and propose a hybrid contextual approach. We then identify appropriate relaxations of contextual constraints for each algorithm component. The effec-tiveness of context relaxation is demonstrated by comparison of three algorithms using a travel data set: a contenxt-ignorant approach, contextual pre-filtering, and our hybrid contextual algorithm. The experiments show that choosing an appro-priate relaxation of the contextual constraints for each component of an algorithm outperforms strict application of the context.
Context-Aware Recommender Systems for Learning: A Survey and Future Challenges
- IEEE Trans. Learn
, 2012
"... Abstract—Recommender systems have been researched extensively by the Technology Enhanced Learning (TEL) community during the last decade. By identifying suitable resources from a potentially overwhelming variety of choices, such systems offer a promising approach to facilitate both learning and teac ..."
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Cited by 13 (0 self)
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Abstract—Recommender systems have been researched extensively by the Technology Enhanced Learning (TEL) community during the last decade. By identifying suitable resources from a potentially overwhelming variety of choices, such systems offer a promising approach to facilitate both learning and teaching tasks. As learning is taking place in extremely diverse and rich environments, the incorporation of contextual information about the user in the recommendation process has attracted major interest. Such contextualization is researched as a paradigm for building intelligent systems that can better predict and anticipate the needs of users, and act more efficiently in response to their behavior. In this paper, we try to assess the degree to which current work in TEL recommender systems has achieved this, as well as outline areas in which further work is needed. First, we present a context framework that identifies relevant context dimensions for TEL applications. Then, we present an analysis of existing TEL recommender systems along these dimensions. Finally, based on our survey results, we outline topics on which further research is needed. Index Terms—Adaptive and Intelligent Educational Systems, Personalized E-Learning, System Applications and Experience. F
Recommendation with differential context weighting
- In The 21st Conference on User Modeling, Adaptation and Personalization (UMAP 2013
, 2013
"... Abstract. Context-aware recommender systems (CARS) adapt their recommen-dations to users ’ specific situations. In many recommender systems, particularly those based on collaborative filtering, the contextual constraints may lead to spar-sity: fewer matches between the current user context and previ ..."
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Cited by 12 (12 self)
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Abstract. Context-aware recommender systems (CARS) adapt their recommen-dations to users ’ specific situations. In many recommender systems, particularly those based on collaborative filtering, the contextual constraints may lead to spar-sity: fewer matches between the current user context and previous situations. Our earlier work proposed an approach called differential context relaxation (DCR), in which different subsets of contextual features were applied in different compo-nents of a recommendation algorithm. In this paper, we expand on our previous work on DCR, proposing a more general approach – differential context weight-ing (DCW), in which contextual features are weighted. We compare DCR and DCW on two real-world datasets, and DCW demonstrates improved accuracy over DCR with comparable coverage. We also show that particle swarm opti-mization (PSO) can be used to efficiently determine the weights for DCW.
Context-aware recommendation based on review mining,” General Co-Chairs
, 2011
"... Recommender systems are important building blocks in many of today’s e-commerce applications including targeted advertising, personalized marketing and information retrieval. In recent years, the importance of contextual information has motivated many researchers to focus on designing systems that p ..."
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Cited by 10 (6 self)
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Recommender systems are important building blocks in many of today’s e-commerce applications including targeted advertising, personalized marketing and information retrieval. In recent years, the importance of contextual information has motivated many researchers to focus on designing systems that produce personalized recommendations in accordance with the available contextual information of users. Compared to the traditional systems that mainly utilize users ’ preference history, context-aware recommender systems provide more relevant results to users. We introduce a contextaware recommender system that obtains contextual information by mining user reviews and combining them with user rating history to compute a utility function over a set of items. An item utility is a measure that shows how much it is preferred according to user’s current context. In our system, the context inference is modeled as a supervised topicmodeling problem in which a set of categories for a contextual attribute constitute the topic set. As an example application, we used our method to mine hidden contextual data from customers ’ reviews of hotels and use it to produce context-aware recommendations. Our evaluations suggest that our system can help produce better recommendations in comparison to a standard kNN recommender system. 1
