Learning to recommend diverse items over implicit feedback on PANDOR

Sidana, Sumit; Laclau, Charlotte; Amini, Massih-Reza

doi:10.1145/3240323.3240400

Cited by 20 publications

(14 citation statements)

References 8 publications

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“…• Pandor (Sidana et al, 2018b) is another publicly available dataset for online recommendation (Sidana et al, 2018a) provided by Purch 1 . The dataset records 2,073,379 clicks generated by 177,366 users of one of the Purch's high-tech website over 9,077 ads they have been shown during one month.…”

Section: Datasetsmentioning

confidence: 99%

“…To estimate the importance of the maximum number of blocks (B) for SAROS b , we explore the dependency between quality metrics MAP@K and NDCG@K on ML-1M and Pandor collections (Figure 4). The latter records the clicks generated by users on one of Purch's high-tech website and it was subject to bot attacks (Sidana et al, 2018a). For this collection, large values of B affects MAP@K while the measure reaches a plateau on ML-1M.…”

Section: Datasetsmentioning

confidence: 99%

See 1 more Smart Citation

Learning over No-Preferred and Preferred Sequence of Items for Robust Recommendation

Aleksandra

Maximov²,

Clausel³

et al. 2021

jair

Self Cite

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In this paper, we propose a theoretically supported sequential strategy for training a large-scale Recommender System (RS) over implicit feedback, mainly in the form of clicks. The proposed approach consists in minimizing pairwise ranking loss over blocks of consecutive items constituted by a sequence of non-clicked items followed by a clicked one for each user. We present two variants of this strategy where model parameters are updated using either the momentum method or a gradient-based approach. To prevent updating the parameters for an abnormally high number of clicks over some targeted items (mainly due to bots), we introduce an upper and a lower threshold on the number of updates for each user. These thresholds are estimated over the distribution of the number of blocks in the training set. They affect the decision of RS by shifting the distribution of items that are shown to the users. Furthermore, we provide a convergence analysis of both algorithms and demonstrate their practical efficiency over six large-scale collections with respect to various ranking measures and computational time.

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Section: Datasetsmentioning

confidence: 99%

Section: Datasetsmentioning

confidence: 99%

Learning over No-Preferred and Preferred Sequence of Items for Robust Recommendation

Aleksandra

Maximov²,

Clausel³

et al. 2021

jair

Self Cite

View full text Add to dashboard Cite

show abstract

“…In recent years, several works have been done to either use implicit feedbacks [21]- [23] or trust propagation [7], [28]- [30] in recommender systems. A well-known recommendation model that incorporates both the explicit and implicit influence of user trust and of item ratings is TrustSVD [12].…”

Section: Related Workmentioning

confidence: 99%

“…Although the social information is complementary to the rating information, the direct social relationship is also very sparse which is similar to user-item ratings. To identify more information used for predicting users' preference, some researchers take advantage of the implicit feedback information [18]- [23]. For example, the ratings on items and the trust values between users are explicit information, and the rated items, trusted friends and trusting friends of users are implicit information.…”

Section: Introductionmentioning

confidence: 99%

Mining Heterogeneous Influence and Indirect Trust for Recommendation

Xiong

Wang

et al. 2020

IEEE Access

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Relationships between users in social networks have been widely used to improve recommender systems. However, actual social relationships are always sparse, which sometimes bring great harm to the performance of recommender systems. In fact, a user may interact with others that he/she does not connect directly, and thus has an impact on these users. To mine abundant information for social recommendation and alleviate the problem of data sparsity, we study the process of trust propagation and propose a novel recommendation algorithm that incorporates multiple information sources into matrix factorization. We first explore heterogeneous influence strength for each pair of linked users and mine indirect trust between users by using trust propagation and aggregation strategy in social networks. Then, explicit and implicit information of user trust and ratings are incorporated into matrix factorization, and the influence of indirect trust is considered in the recommendation process. Experimental results show that the proposed model achieves better performance than some state-of-the-art recommendation models in terms of accuracy and relieves the cold-start problem.

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“…To obtain as much useful information as possible to predict user preferences, some researchers have begun to use implicit feedback information [11][12][13][14][15][16]. Liu et al [17] proposed a novel user similarity calculation method, which not only uses the rating data of text information but also includes the preferences of registered users, which improves the accuracy of recommendation and can alleviate the problem of data sparsity.…”

Section: Introductionmentioning

confidence: 99%

A Recommendation Approach for Rating Prediction Based on User Interest and Trust Value

Hai-long

Sun

Cheng

et al. 2021

Computational Intelligence and Neuroscience

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Collaborative filtering recommendation algorithm is one of the most researched and widely used recommendation algorithms in personalized recommendation systems. Aiming at the problem of data sparsity existing in the traditional collaborative filtering recommendation algorithm, which leads to inaccurate recommendation accuracy and low recommendation efficiency, an improved collaborative filtering algorithm is proposed in this paper. The algorithm is improved in the following three aspects: firstly, considering that the traditional scoring similarity calculation excessively relies on the common scoring items, the Bhattacharyya similarity calculation is introduced into the traditional calculation formula; secondly, the trust weight is added to accurately calculate the direct trust value and the trust transfer mechanism is introduced to calculate the indirect trust value between users; finally, the user similarity and user trust are integrated, and the prediction result is generated by the trust weighting method. Experiments show that the proposed algorithm can effectively improve the prediction accuracy of recommendations.

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Learning to recommend diverse items over implicit feedback on PANDOR

Cited by 20 publications

References 8 publications

Learning over No-Preferred and Preferred Sequence of Items for Robust Recommendation

Learning over No-Preferred and Preferred Sequence of Items for Robust Recommendation

Mining Heterogeneous Influence and Indirect Trust for Recommendation

A Recommendation Approach for Rating Prediction Based on User Interest and Trust Value

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