Social Recommender Systems

Groh, Georg; Birnkammerer, Stefan; Köllhofer, Valeria

doi:10.1007/978-3-642-25694-3_1

Cited by 20 publications

(8 citation statements)

References 48 publications

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“…Besides, answers to all questions are higher in G3-G6 than in G2 and the difference is again statistically significant. This result allows us to validate H2 and to conclude the need of including a social component in group explanations, a fact also observed by other researchers in individual recommenders (Groh et al, 2012;Sharma and Cosley, 2013;Knijnenburg et al, 2012) but never applied for group recommenders. Furthermore, answers to all questions are higher in G6 than in G3-G5 and the difference is also statistically significant.…”

Section: Q1supporting

confidence: 80%

“…This new perspective enables the study of explanations related to user's social behaviour within the group. Social explanations have been previously included for individual recommenders (Groh et al, 2012;Sharma and Cosley, 2013;Knijnenburg et al, 2012) but, to the best of the authors' knowledge, never for group recommenders. In particular the proposed approach focuses on explaining the recommendation process followed by the HappyMovie system (Quijano-Sánchez et al, 2014), that consists of the evaluation of different social factors (user's personality, tie strength between users and previous satisfaction) on top of the typical nonsocial aggregation techniques.…”

Section: Discussionmentioning

confidence: 99%

“…2) These studies (Groh et al, 2012;Sharma and Cosley, 2013;Knijnenburg et al, 2012;Cialdini and Trost, 1998) only involve the inclusion of one social component, related mainly to tie strength. However, as we have mentioned before, HappyMovie -the system that we use as baseline for the explanation generation-takes into account two additional factors: personality and satisfaction.…”

Section: Motivationmentioning

confidence: 99%

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Make it personal: A social explanation system applied to group recommendations

Quijano-Sánchez

Sauer

Recio-García

et al. 2017

Expert Systems with Applications

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Recommender systems help users to identify which items from a variety of choices best match their needs and preferences. In this context, explanations act as complementary information that can help users to better comprehend the system's output and to encourage goals such as trust, confidence in decision-making or utility. In this paper we propose a Personalized Social Individual Explanation approach (PS IE). Unlike other expert systems the PS IE proposal novelly includes explanations about the system's group recommendation and explanations about the group's social reality with the goal of inducing a positive reaction that leads to a better perception of the received group recommendations. Among other challenges, we uncover a special need to focus on "tactful" explanations when addressing users' personal relationships within a group and to focus on personalized reassuring explanations that encourage users to accept the presented recommendations. Besides, the resulting intelligent system significatively increases users' intent (likelihood) to follow the recommendations, users' satisfaction and the system's efficiency and trustworthiness.

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Section: Q1supporting

confidence: 80%

Section: Discussionmentioning

confidence: 99%

Section: Motivationmentioning

confidence: 99%

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Make it personal: A social explanation system applied to group recommendations

Quijano-Sánchez

Sauer

Recio-García

et al. 2017

Expert Systems with Applications

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“…There is obviously a huge literature in computer science on recommendation systems that we will not attempt to summarize (see e.g. the survey by Groh et al, 2012). Applications of the axiomatic approach based on social choice concepts seem scarce in this literature (Pennock et al, 2000;Altman and Tennenholtz, 2007).…”

Section: Introductionmentioning

confidence: 99%

Tailored recommendations

2020

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Many popular internet platforms give personalized recommendations to their users, based on other users who have provided similar ratings for some items. We propose a novel approach to such recommendation systems by viewing a recommendation as a way to extend an agent's preferences to items she has not yet rated, by means of some aggregate of other agents' preferences. These extension and aggregation requirements are expressed by an Acceptance and a Pareto principle, respectively. We show through examples that so-called collaborative filtering systems used by popular platforms typically violate the Pareto principle. We then develop a formal model within which we identify the recommendation systems satisfying the above two principles. A central feature of this model is the use of incomplete preference relations to handle agents who have not rated all items.

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“…Representation, similarity and ranking algorithms from the Case-Based Reasoning (CBR) community has naturally made a significant contribution to recommender systems research [18,23]. The dawn of the social web creates many new opportunities for recommendation algorithms and so the emergence of social recommender systems [9,12].…”

Section: Introductionmentioning

confidence: 99%

Sentiment and Preference Guided Social Recommendation

Chen

Ferrer

Wiratunga

et al. 2014

Case-Based Reasoning Research and Development

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Abstract. Social recommender systems harness knowledge from social experiences, expertise and interactions. In this paper we focus on two such knowledge sources: sentiment-rich user generated reviews; and preferences from purchase summary statistics. We formalise the integration of these knowledge sources by mixing a novel aspect-based sentiment ranking with a preference ranking. We demonstrate the utility of our proposed formalism by conducting a comparative analysis on data extracted from Amazon.com. In particular we show that the performance of the proposed aspect based sentiment analysis algorithm is superior to existing aspect extraction algorithms and that combining this with preference knowledge leads to better recommendations.

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Social Recommender Systems

Cited by 20 publications

References 48 publications

Make it personal: A social explanation system applied to group recommendations

Make it personal: A social explanation system applied to group recommendations

Tailored recommendations

Sentiment and Preference Guided Social Recommendation

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