Incorporating contextual information in recommender systems using a multidimensional approach

By Gediminas Adomavicius, Ramesh Sankaranarayanan, Shahana Sen, Alexander Tuzhilin. TOIS, 2005

1,308 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Information Systems, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/4a4244bb8d01f489892ae2423c83ee60bd035c00

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In Computer Vision and Pattern Recognition

In Information Systems

In Management Science and Operations Research

Citations in the corpus

Mobile recommender systems in tourism (2014, background): “Adomavicius et al. (2005) proposed a contextualized view on ratings, giving rise to CARS.”
General factorization framework for context-aware recommendations (2014, background): “Context can be the location or time of the interaction, the device on which the interaction was performed, or any other parameters that may influence the user preference, including weather, social network, referral’s link, search keyword, etc.”
Time-aware point-of-interest recommendation (2013, background): “Our work is related to the existing work [1] on integrating context into recommender systems.”
Fast ALS-based tensor factorization for context-aware recommendation from implicit feedback (2012, background): “Context-aware recommender systems [12] emerged as an important research topic in the last years and entire workshops are devoted to this topic on major conferences (CARS series started in 2009 [13], CAMRA in 2010 [14]).”
A new similarity measure for collaborative filtering to alleviate the new user cold-starting problem (2008, background): “For example, keywords of purchased books of a user might be used to find other books that contain the same or similar keywords for recommendation [1,3,13,17,20,22].”
Music recommendations with temporal context awareness (2010, methodology): “Following this trend, there are approaches to improve recommendations by incorporation of contextual information in the process [2].”
Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions (2005, methodology): “As was argued in [2], [3], many of the two-dimensional recommendation algorithms cannot be directly extended to the multidimensional case.”
TFMAP: optimizing MAP for top-n context-aware recommendation (2012, background): “Typically, recommender systems have access to additional information about the user-item interactions, such as the context that is associated with the user-item interaction [1].”
Context-aware recommender systems (2008, neutral): “Contextual information was also defined in [3] as follows.”
Fast context-aware recommendations with factorization machines (2011, methodology): “dataset [1] as well as the Yahoo! Webscope dataset(6) enriched with context-aware information by [5].”
Developing a Price-Sensitive Recommender System to Improve Accuracy and Business Performance of Ecommerce Applications (2015, methodology): “Most of the research in this area has demonstrated that including additional contextual dimensions can improve recommendations accuracy if properly deployed 19 .”
Collaborative Filtering beyond the User-Item Matrix (2014, background): “In the past few decades, signiﬁcant progress has been made in recommender system performance by deploying CF to exploit user-item (U-I) relations, which are typically encoded in a U-I matrix [Adomavicius and Tuzhilin 2005; Burke 2002; Ekstrand et al. 2011; Herlocker et al. 2004].”
Explaining the user experience of recommender systems (2012, background): “Even more so, when evaluating the relative merit of novel recommendation approaches such as context-aware algorithms (Adomavicius et al. 2005) and recommenders using social networks (Kautz et al. 1997), one has to rely on more sophisticated ways of measuring the full user experience, and our…”
Recommender systems: from algorithms to user experience (2012, background): “5.2 Recommender context Adomavicius et al. 2005 argues that in different contexts, such as at different times of the day, users may be interested in different types of recommendations.”
Identifying and utilizing contextual data in hybrid recommender systems (2010, background): “[2] combined the concepts of contextual and hybrid recommender systems and created models for dimensionality reduction of both multidimensional content data, as well as its contextual counterparts, and then combined these into a unified recommendation model.”
Bias in algorithmic filtering and personalization (2013, background): “Then the recommendation mechanism presents to the user one or more descriptions of objects (e.g., books, people, movies) that may be of interest (Adomavicius et al. 2005; Garcia-Molina et al. 2011).”