Learning User Preference Patterns for Top-N Recommendations

Ren, Yongli; Li, Gang; Zhou, Wanlei

doi:10.1109/wi-iat.2012.102

Cited by 10 publications

(4 citation statements)

References 21 publications

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“…A similar approach has been used in [25], where the authors exploited a reinforcement learning algorithm in order to customize the rendering of advertisements in Web pages, on the basis of users' preferences. Learning and predicting users' preferences, so as to drive recommendation systems is also the main aim of [26]. The reward/punishment approach has been exploited also in [27], where the authors propose the use of the Q-learning algorithm for modeling the behavior of agents in simulations.…”

Section: Introductionmentioning

confidence: 99%

Automatic web content personalization through reinforcement learning

Ferretti

Mirri

Prandi

et al. 2016

Journal of Systems and Software

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This paper deals with the automatic adaptation of Web contents. It is recognized that quite often users need some personalized adaptations to access Web contents. This is more evident when we focus on people with some accessibility needs. Based on the user profile, it is possible to transcode or modify contents (e.g., adapt text fonts) so as to meet the user preferences. The problem is that applying such a kind of transformations to the whole content might significantly alter Web pages that might become unreadable, hence making matters worse. We present a system that employs Web intelligence to perform automatic adaptations on single elements composing a Web page. A reinforcement learning algorithm is utilized to manage user profiles. We evaluate our system through simulation and a real assessment where elderly users where asked to use for a time period our system prototype. Results confirm the feasibility of the proposal.who are equipped with devices with different capabilities, such as tablets, smart-phones, and smart TVs. In this context, both readability and legibility are affected by different issues, such as Web text characteristics [4,5,6] and users' abilities [7,8,9]. This work presents a system, called ExTraS (EXperiential TRAnscoding System [1,10,11]), and thought to improve Web content legibility. The approach adapts some characteristics, in particular the text formatting ones (e.g. font size, font face, luminance contrasts), according to users' preferences and needs. ExTraS tracks users' behavior to learn and model their preferences and to automatically provide the best adaptation, tailored for each user, predicting his/her needs. Such an adaptation is based on a machine learning mechanism (Reinforcement Learning), exploiting the idea of reward/punishment. We have built a prototype system and we have evaluated it by means of a simulation assessment and tests with real users. Both of them confirm the viability of our proposal.The remainder of the paper is organized as follows. Section 2 describes background and related work, while Section 3 presents the architecture of our system, detailing how it profiles users, how it applies the machine learning mechanism and how it adapts the content. Section 4 introduces the prototype we have developed, showing some screenshots. Section 5 reports the simulation assessment and the tests with users we have performed, detailed the obtained results. Finally, Section 6 concludes the paper illustrating main findings and further work. Background and related workOur work takes into account several issues and has been based on many related work. This section aims to briefly describe the most significant ones, which are related to improving Web pages legibility with font adaptation, adaptation and personalization of digital and Web contents and services, and the use of machine learning techniques and algorithms to track and understand users' experiences and behaviors.According to the definitions in the literature, legibility is related to perceiving text by distinguishing e...

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Section: Introductionmentioning

confidence: 99%

Automatic web content personalization through reinforcement learning

Ferretti

Mirri

Prandi

et al. 2016

Journal of Systems and Software

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“…WENG et al proposed a new recommendation model, which studied the implicit relationships between user's item preference and additional category preference together to alleviate the cold start problem [18]. Ren et al defined the user preference as a time-ordered distribution sequence on item categories to complete T0P-N recommendation [19], [20]. A drama was represented by the sequences of subcategories and was recommended using similarities between the user interest and drama [32].…”

Section: B Studying Correlation Between the User's Preferences And Imentioning

confidence: 99%

“…The existing category-driven methods can be mainly divided into four ctegories: (1) Improved CF models, which add item category information as additional information to the traditional CF model [11], [16], [17]. (2) The methods, which study the relationships between the users' preferences and item categories and recommend items according to the relationships [18]- [20]. 3The methods, which calculate the correlation between item categories by considering the ratings, and recommend the items according to it [10], [21].…”

Section: Introductionmentioning

confidence: 99%

CCSMR: A Combinatorial Category Space-Based Model for Recommendation

et al. 2019

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Various side information has been exploited in recommender systems to help users finding items they prefer to alleviate data sparsity. Because item category can be used to view the user's preference in a high-level scope and an item can have more than one associated category, the existing works leverage this characteristic, that enriches the information density of item, to further improve the recommendation performance. However, the existing works either treat item categories or their correlations as additional information to compute the similarity between users and items, or introduce category hierarchy as a latent factor into matrix factorization (MF) models. In these works, each item corresponds to a category set, that makes it too difficult to utilize item category's potential and valuable information. In this paper, we propose a novel combinatorial category space based recommendation model, which can fully exploit item category information and reduce the difficulty by converting the relationship between item and its categories from one-to-many to one-to-one. It considers a combinatorial category associated with an item, not each of them individually. Specifically, we first define a combinatorial category space (CCS), which is viewed as a subset-superset lattice of item categories. Then semantic relations and semantic distances are defined to model a user preference, and a compound graph (CoGraph) is constructed to model users' interactive behaviors in CCS, respectively. Finally, two recommendation models, CCSRank and CCSMF, are exploited on user interactive graph. We carry out extensive experimental evaluations on two real datasets to show the effectiveness of our model.

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“…A preliminary work [38] of this research appears in the proceedings of the 2012 IEEE/WIC/ACM International Conferences on Web Intelligence, Macau, China, pp. 137-144, 2012.…”

Section: Introductionmentioning

confidence: 99%

Modelling personal preferences for Top-N movie recommendations

Ren

Zhou

2014

Web Intelligence and Agent Systems: An International Journal

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Modelling the temporal dynamics of personal preferences is still under-developed despite the rapid development of personalization. In this paper, we observe that the user preference styles tend to change regularly following certain patterns in the context of movie recommendation systems. Therefore, we propose a Preference Pattern model to capture the user preference styles and their temporal dynamics, and apply this model to improve the accuracy of the Top-N movie recommendations. Precisely, a preference pattern is defined as a set of user preference styles sorted in a time order. The basic idea is to model user preference styles and their temporal dynamics by constructing a representative subspace with an Expectation-Maximization (EM)-like algorithm, which works in an iterative fashion by refining the global and the personal preference styles simultaneously. Then, the degree which the recommendations match the active user's preference styles, can be estimated by measuring its reconstruction error from its projection on the representative subspace. The experiment results indicate that the proposed model is robust to the data sparsity problem, and can significantly outperform the state-of-the-art algorithms on the Top-N movie recommendations in terms of accuracy.

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Learning User Preference Patterns for Top-N Recommendations

Cited by 10 publications

References 21 publications

Automatic web content personalization through reinforcement learning

Automatic web content personalization through reinforcement learning

CCSMR: A Combinatorial Category Space-Based Model for Recommendation

Modelling personal preferences for Top-N movie recommendations

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