PolyLens: A Recommender System for Groups of Users

O'Connor, Mark; Cosley, Dan; Konstan, Joseph A.; Riedl, John

doi:10.1007/0-306-48019-0_11

Cited by 297 publications

(281 citation statements)

References 21 publications

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“…Polylens [16] in movies domain. Or, regarding recommendations of restaurants for groups, Pocket Restaurant Finder [17].…”

Section: Literature Reviewmentioning

confidence: 99%

“…So, if for example we selected Marilyn Monroe movies the actor's field would be useless as she is no longer making movies and there would not be any possible comparison between fields. 16 .…”

Section: Content Based Estimationmentioning

confidence: 99%

“…Possible aggregation functions include least misery (where the minimum is taken), most pleasure (where the maximum is taken) or average satisfaction (where the average of the predicted ratings of 16 Extracted genres correspond to the genres used by the web the Web Crawler searches (http://www.guiadelocio.com). 17 We are aware of the limitations of this experiment given the low number of respondents and intend to make further analysis now that we have gathered more information as a result of the experiment carried out in this paper.…”

Section: Group Recommendation Modulementioning

confidence: 99%

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Development of a group recommender application in a Social Network

Quijano-Sánchez

Díaz-Agudo

Recio-García

2014

Knowledge-Based Systems

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In today's society, recommendations are becoming increasingly important. With the advent of the Social Web and the growing popularity of Social Networks, where users explicitly provide personal information and interact with others and the system, it is becoming clear that the key for the success of recommendations is to develop new strategies which focus on social recommendations leveraged by these new sources of knowledge. In our work, we focus on group recommender systems. These systems traditionally suffer from a number of shortcomings that hamper their effectiveness. In this paper we continue our research, that focuses on improving the overall quality of group recommendations through the addition of social knowledge to existing recommendation strategies. To do so, we use the information stored in Social Networks to elicit social factors following two approaches: the cognitive modeling approach, that studies how people's way of thinking predisposes their actions; and the social approach, that studies how people's relationships predispose their actions. We show the value of using models of social cognition extracted from Social Networks in group recommender systems through the instantiation of our model into a real-life Facebook movie recommender application.

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“…Polylens [16] in movies domain. Or, regarding recommendations of restaurants for groups, Pocket Restaurant Finder [17].…”

Section: Literature Reviewmentioning

confidence: 99%

Section: Content Based Estimationmentioning

confidence: 99%

Section: Group Recommendation Modulementioning

confidence: 99%

See 1 more Smart Citation

Development of a group recommender application in a Social Network

Quijano-Sánchez

Díaz-Agudo

Recio-García

2014

Knowledge-Based Systems

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show abstract

“…Typically, recommendation approaches are distinguished between: content-based, that recommend items similar to those the user previously preferred (e.g., [17,13]), collaborative filtering, that recommend items that users with similar preferences liked (e.g., [11,8]) and hybrid, that combine content-based and collaborative ones (e.g., [5]). Several extensions have been proposed, such as employing multi-criteria ratings (e.g., [2]) and defining recommendations for groups (e.g., [4,15,14]). Recently, there are also approaches focusing on enhancing recommendations with further contextual information (e.g., [3,16]).…”

Section: Related Workmentioning

confidence: 99%

A Framework for Time-Aware Recommendations

Stefanidis

Ntoutsi

Nørvåg

et al. 2012

Lecture Notes in Computer Science

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Abstract. Recently, recommendation systems have received significant attention. However, most existing approaches focus on recommending items of potential interest to users, without taking into consideration how temporal information influences the recommendations. In this paper, we argue that time-aware recommendations need to be pushed in the foreground. We introduce an extensive model for time-aware recommendations from two perspectives. From a fresh-based perspective, we propose using a suite of aging schemes towards making recommendations mostly depend on fresh and novel user preferences. From a context-based perspective, we focus on providing different suggestions under different temporal specifications. The proposed strategies are experimentally evaluated using real movies ratings.

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“…In fact, this is the essence of the well-known collaborative recommender systems [2,9,13], where items are recommended to a certain user concerning those of her interests shared with other users or according to opinions, comparatives and ratings of items given by similar users. However, in typical approaches, the comparison between users and items is done globally, in such a way that partial, but strong and useful similarities may be missed.…”

Section: Introductionmentioning

confidence: 99%

Multilayered Semantic Social Network Modeling by Ontology-Based User Profiles Clustering: Application to Collaborative Filtering

Cantador

Castells

2006

Managing Knowledge in a World of Networks

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Esta es la versión de autor de la comunicación de congreso publicada en: This is an author produced version of a paper published in: Abstract. We propose a multilayered semantic social network model that offers different views of common interests underlying a community of people. The applicability of the proposed model to a collaborative filtering system is empirically studied. Starting from a number of ontology-based user profiles and taking into account their common preferences, we automatically cluster the domain concept space. With the obtained semantic clusters, similarities among individuals are identified at multiple semantic preference layers, and emergent, layered social networks are defined, suitable to be used in collaborative environments and content recommenders.

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PolyLens: A Recommender System for Groups of Users

Cited by 297 publications

References 21 publications

Development of a group recommender application in a Social Network

Development of a group recommender application in a Social Network

A Framework for Time-Aware Recommendations

Multilayered Semantic Social Network Modeling by Ontology-Based User Profiles Clustering: Application to Collaborative Filtering

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