Learning to Rank with Trust and Distrust in Recommender Systems

Rafailidis, Dimitrios; Crestani, Fábio

doi:10.1145/3109859.3109879

Cited by 42 publications

(18 citation statements)

References 54 publications

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“…Lee et al [24] assumed that the user-item matrix is low rank within certain neighborhoods of the metric space and minimized a pair-wise loss function. Rafailidis and Crestani [32] proposed a model focusing on trust and distrust between users. They pushed the relevant items of users and their friends at the top of the list, while ranking low those of their foes.…”

Section: Collaborative Rankingmentioning

confidence: 99%

A Collaborative Ranking Model with Multiple Location-based Similarities for Venue Suggestion

Aliannejadi

Rafailidis

Crestani

2018

Proceedings of the 2018 ACM SIGIR International Conference on Theory of Information Retrieval

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Recommending venues plays a critical rule in satisfying users' needs on location-based social networks. Recent studies have explored the idea of adopting collaborative ranking (CR) for recommendation, combining the idea of learning to rank and collaborative filtering. However, CR suffers from the sparsity problem, mainly because it associates similar users based on exact matching of the venues in their check-in history. Even though research in collaborative filtering has shown that considering auxiliary information such as geographical influence, helps the model to alleviate the sparsity problem, the same direction still needs to be explored in CR. In this work, we present a CR framework that focuses on the top of the ranked list while integrating an arbitrary number of similarity functions between venues as it learns the model's parameters. We further introduce three example similarity measures based on venues' contents and locations. Incorporating cross-venue similarity measures into the model enhances the latent associations between users as similar venues are also taken into account while associating users with each other. Our experiments on the TREC Contextual Suggestion dataset show that our proposed CR model beats other state-of-the-art venue suggestion methods. ACM Reference Format: Mohammad Aliannejadi, Dimitrios Rafailidis, and Fabio Crestani. 2018. A Collaborative Ranking Model with Multiple Location-based Similarities for Venue Suggestion.

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Section: Collaborative Rankingmentioning

confidence: 99%

A Collaborative Ranking Model with Multiple Location-based Similarities for Venue Suggestion

Aliannejadi

Rafailidis

Crestani

2018

Proceedings of the 2018 ACM SIGIR International Conference on Theory of Information Retrieval

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“…Socially-Aware Recommendation: Leveraging social networks can help us understand user-user relationships and improve the performance of item recommendation [7,8,10,16]. The social network is usually based on friendship or 'trust' relationships.…”

Section: Related Workmentioning

confidence: 99%

Learning consumer and producer embeddings for user-generated content recommendation

Kang

McAuley

2018

Proceedings of the 12th ACM Conference on Recommender Systems

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User-Generated Content (UGC) is at the core of web applications where users can both produce and consume content. This differs from traditional e-Commerce domains where content producers and consumers are usually from two separate groups. In this work, we propose a method CPRec (consumer and producer based recommendation), for recommending content on UGC-based platforms. Specifically, we learn a core embedding for each user and two transformation matrices to project the user's core embedding into two 'role' embeddings (i.e., a producer and consumer role). We model each interaction by the ternary relation between the consumer, the consumed item, and its producer. Empirical studies on two large-scale UGC applications show that our method outperforms standard collaborative filtering methods as well as recent methods that model producer information via item features.

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“…This listwise approach employs the derivative estimation of NDCG during training of LtR algorithm to assign the parameters of regression trees. Learning to rank model based recommender system [31] utilizes the trust and distrust relationships of the users and presents the most relevant items of the users and their friends at the top of the list. It presents the weighting strategy and captures the correlations between the user preferences based on the trust of the friends and distrust of the foes.…”

Section: Ranking Based Recommender Systemsmentioning

confidence: 99%

Diversity-Ensured Semantic Movie Recommendation by Applying Linked Open Data

Srinivasan¹,

Mani²

2018

IJIES

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Abstract:The recommender system becomes a significant research area due to the popularity of the social web. Traditional semantic recommender systems deliver poor performance when balancing the recommendation accuracy and diversity. Also, the rank-based recommendation methods lack to obtain the coverage of the entire preferences of the user in the top-N recommendation list. Thus, this paper presents the Diversity-Ensured Semantic-aware Item REcommendation (DESIRE) that deals with the consistent and reliable knowledge source to significantly improve the quality and provide the diversity-ensured top-N recommendation list. The DESIRE approach builds the semantically relevant graphs such as movie-centric and user rating-centric graph with the help of both the Linked Open Data (LOD) and the explicit ratings of the users. By extracting the semantic-path based features from the user rating-centric graph, it executes the ranking algorithm for the top-N movie recommendation. Moreover, the diversity-aware re-ranking tends to maintain the trade-off between the diversity and accuracy in the top-N recommendation.

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Learning to Rank with Trust and Distrust in Recommender Systems

Cited by 42 publications

References 54 publications

A Collaborative Ranking Model with Multiple Location-based Similarities for Venue Suggestion

A Collaborative Ranking Model with Multiple Location-based Similarities for Venue Suggestion

Learning consumer and producer embeddings for user-generated content recommendation

Diversity-Ensured Semantic Movie Recommendation by Applying Linked Open Data

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