Diverse personalized recommendations with uncertainty from implicit preference data with the Bayesian Mallows model

Liu, Qinghua; Reiner, Andrew H.; Frigessi, Arnoldo; Scheel, Ida

doi:10.1016/j.knosys.2019.104960

Cited by 12 publications

(9 citation statements)

References 20 publications

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“…In conventional top-K recommendation, a large body of works adopt collaborative filtering, e.g., matrix factorization to utilize implicit interactions to infer the links between users and items [17,22,29]. The predominant problem is usually learning latent factors for users and items, and predicting user-item interactions by ranking the inner product of user and item embeddings [17].…”

Section: Attribute Modelling For Recommendationmentioning

confidence: 99%

Try This Instead: Personalized and Interpretable Substitute Recommendation

Chen¹,

Yin²,

Ye³

et al. 2020

Preprint

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As a fundamental yet significant process in personalized recommendation, candidate generation and suggestion effectively help users spot the most suitable items for them. Consequently, identifying substitutable items that are interchangeable opens up new opportunities to refine the quality of generated candidates. When a user is browsing a specific type of product (e.g., a laptop) to buy, the accurate recommendation of substitutes (e.g., better equipped laptops) can offer the user more suitable options to choose from, thus substantially increasing the chance of a successful purchase. However, existing methods merely treat this problem as mining pairwise item relationships without the consideration of users' personal preferences. Moreover, the substitutable relationships are implicitly identified through the learned latent representations of items, leading to uninterpretable recommendation results.In this paper, we propose attribute-aware collaborative filtering (A2CF) to perform substitute recommendation by addressing issues from both personalization and interpretability perspectives. In A2CF, instead of directly modelling user-item interactions, we extract explicit and polarized item attributes from user reviews with sentiment analysis, whereafter the representations of attributes, users, and items are simultaneously learned. Then, by treating attributes as the bridge between users and items, we can thoroughly model the user-item preferences (i.e., personalization) and item-item relationships (i.e., substitution) for recommendation. In addition, A2CF is capable of generating intuitive interpretations by analyzing which attributes a user currently cares the most and comparing the recommended substitutes with her/his currently browsed items at an attribute level. The recommendation effectiveness and interpretation quality of A2CF are further demonstrated via extensive experiments on three real-life datasets. CCS CONCEPTS• Information systems → Recommender systems.

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Section: Attribute Modelling For Recommendationmentioning

confidence: 99%

Try This Instead: Personalized and Interpretable Substitute Recommendation

Chen¹,

Yin²,

Ye³

et al. 2020

Preprint

View full text Add to dashboard Cite

show abstract

“…Recently, Shah (2021) collected several approaches to build probability distributions over permutations. Liu et al (2019b) present additional options, in particular the Mallows model (Mallows, 1957). The difficulty of course originates from the dimension of P n for larger n. In inference, the incompleteness of the data means that appropriate conditional distributions over P n need to be constructed.…”

Section: Introductionmentioning

confidence: 99%

“…Several right invariant distances are considered in Vitelli et al (2018), among which the l 1 -norm, called foot-rule, and the l 2 -norm, called the Spearman distance. Given a ranking of some of the n items (partial ranking), a set of pair comparisons or a collection of clicked items (Liu et al, 2019b), Bayesian inference can be performed by augmentation and Markov Chain Monte Carlo (MCMC). These papers demonstrate that accurate and diverse personalized recommendations can be made, with interpretable and well calibrated uncertainty estimation.…”

Section: Introductionmentioning

confidence: 99%

“…These papers demonstrate that accurate and diverse personalized recommendations can be made, with interpretable and well calibrated uncertainty estimation. However, both Vitelli et al (2018) and Liu et al (2019b) use MCMC for inference, which shows slow convergence for large n and N . The discrete posterior distribution is also highly multimodal.…”

Section: Introductionmentioning

confidence: 99%

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Pseudo-Mallows for Efficient Probabilistic Preference Learning

Liu¹,

Vitelli²,

Mannino³

et al. 2022

Preprint

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We propose the Pseudo-Mallows distribution over the set of all permutations of n items, to approximate the posterior distribution with a Mallows likelihood. The Mallows model has been proven to be useful for recommender systems where it can be used to learn personal preferences from highly incomplete data provided by the users. Inference based on MCMC is however slow, preventing its use in real time applications. The Pseudo-Mallows distribution is a product of univariate discrete Mallows-like distributions, constrained to remain in the space of permutations. The quality of the approximation depends on the order of the n items used to determine the factorization sequence. In a variational setting, we optimise the variational order parameter by minimising a marginalized KL-divergence. We propose an approximate algorithm for this discrete optimization, and conjecture a certain form of the optimal variational order that depends on the data. Empirical evidence and some

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“…Among the many recommendation technologies, content-based recommendation and collaborative filtering recommendation are the most studied. Content-based recommendation is the continuation and development of information filtering technology [12,13]. e system does not need to obtain users' comments on projects but only learns the content information of users' historical selection projects to recommend new projects.…”

Section: Introductionmentioning

confidence: 99%

Personalized Music Recommendation Simulation Based on Improved Collaborative Filtering Algorithm

Hui¹,

2020

Complexity

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Collaborative filtering technology is currently the most successful and widely used technology in the recommendation system. It has achieved rapid development in theoretical research and practice. It selects information and similarity relationships based on the user’s history and collects others that are the same as the user’s hobbies. User’s evaluation information is to generate recommendations. The main research is the inadequate combination of context information and the mining of new points of interest in the context-aware recommendation process. On the basis of traditional recommendation technology, in view of the characteristics of the context information in music recommendation, a personalized and personalized music based on popularity prediction is proposed. Recommended algorithm is MRAPP (Media Recommendation Algorithm based on Popularity Prediction). The algorithm first analyzes the user’s contextual information under music recommendation and classifies and models the contextual information. The traditional content-based recommendation technology CB calculates the recommendation results and then, for the problem that content-based recommendation technology cannot recommend new points of interest for users, introduces the concept of popularity. First, we use the memory and forget function to reduce the score and then consider user attributes and product attributes to calculate similarity; secondly, we use logistic regression to train feature weights; finally, appropriate weights are used to combine user-based and item-based collaborative filtering recommendation results. Based on the above improvements, the improved collaborative filtering recommendation algorithm in this paper has greatly improved the prediction accuracy. Through theoretical proof and simulation experiments, the effectiveness of the MRAPP algorithm is demonstrated.

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Diverse personalized recommendations with uncertainty from implicit preference data with the Bayesian Mallows model

Cited by 12 publications

References 20 publications

Try This Instead: Personalized and Interpretable Substitute Recommendation

Try This Instead: Personalized and Interpretable Substitute Recommendation

Pseudo-Mallows for Efficient Probabilistic Preference Learning

Personalized Music Recommendation Simulation Based on Improved Collaborative Filtering Algorithm

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