Typicality-Based Collaborative Filtering Recommendation

Cai, Yi; Leung, Ho-fung; Li, Qing; Min, Huaqing; Tang, Jie; Li, Juanzi

doi:10.1109/tkde.2013.7

Cited by 190 publications

(72 citation statements)

References 38 publications

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“…Then we get Table 6 to describe information of the training set. In Table6, every movie is classified to a group with high membership which is related with the user's appearance frequency 5 . We also use this process to obtain the membership of movie in testing set.…”

Section: Empirical Results and Discussionmentioning

confidence: 99%

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Fuzzy prototype classifier based on items and its application in recommender system

Mei¹,

Gong²,

Li³

2017

IJCIS

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Currently, recommender systems (RS) are incorporating implicit information from social circle of the Internet. The implicit social information in human mind is not easy to reflect in appropriate decision making techniques. This paper consists of 2 contributions. First, we develop an item-based prototype classifier (IPC) in which a prototype represents a social circle's preferences as a pattern classification technique. We assume the social circle which distinguishes with others by the items their members like. The prototype structure of the classifier is defined by two2-dimensional matrices. We use information gain and OWA aggregator to construct a feature space. The item-based classifier assigns a new item to some prototypes with different prototypicalities. We reform a typical data set-Iris data set in UCI Machine Learning Repository to verify our fuzzy prototype classifier. The second proposition of this paper is to give the application of IPC in recommender system to solve new item cold-start problems. We modify the dataset of MovieLens to perform experimental demonstrations of the proposed ideas.

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Section: Empirical Results and Discussionmentioning

confidence: 99%

“…that are valuable to the user [1,2]. Content-based [3,4] or collaborative filtering(CF) [5] techniques are commonly used techniques. CF is the most popular approach used for recommender systems.…”

Section: Introductionmentioning

confidence: 99%

Fuzzy prototype classifier based on items and its application in recommender system

Mei¹,

Gong²,

Li³

2017

IJCIS

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“…9. The upper red curve 4 In Fig. 7 we report the results of word-adoption behavior prediction on sci-comp dataset.…”

Section: Continuous Vs Discrete Modelingmentioning

confidence: 99%

“…In the literature, it gets extensively studied in the area of recommender systems [1,16], which focuses on recommending the most appropriate items to users based on their past adoption behavior data. The collaborative filtering technique [20] has been widely adopted and numerous methods have been proposed [10,26,18,13,4]. With the emergence of online social networks, the social relationships are found beneficial for such tasks [11,9,14,27], because friends may influence each other and thus tend to exhibit similar behaviors [7].…”

Section: Related Workmentioning

confidence: 99%

Inferring continuous dynamic social influence and personal preference for temporal behavior prediction

Zhang

Wang

et al. 2014

Proc. VLDB Endow.

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It is always attractive and challenging to explore the intricate behavior data and uncover people's motivations, preference and habits, which can greatly benefit many tasks including link prediction, item recommendation, etc. Traditional work usually studies people's behaviors without time information in a static or discrete manner, assuming the underlying factors stay invariant in a long period. However, we believe people's behaviors are dynamic, and the contributing factors including the social influence and personal preference for behaviors are varying continuously over time. Such continuous dynamics convey important knowledge about people's behavior patterns; ignoring them would lead to inaccurate models.In this work, we address the continuous dynamic modeling of temporal behaviors. To model the fully continuous temporal dynamics of behaviors and the underlying factors, we propose the DP-Space, a dynamic preference probability space, which can capture their smooth variation in various shapes over time with flexible basis functions. Upon that we propose a generative dynamic behavior model, ConTyor, which considers the temporal item-adoption behaviors as joint effect of dynamic social influence and varying personal preference over continuous time. We also develop effective inference methods for ConTyor and present its applications.We conduct a comprehensive experimental study using real-world datasets to evaluate the effectiveness of our model and the temporal modeling. Results verify that ConTyor outperforms existing state-of-the-art static and temporal models in behavior predictions. Moreover, in our detailed study on temporal modeling, we show that temporal modeling is superior to static approaches and modeling over continuous time is further better than that over discrete time. We also demonstrate that the ancient behavior data can still become important and beneficial if modeled well.

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“…Content-based methods [5], [26] adopt the profile of the users or products for recommendation. CF based methods [7], [11], [13], [18], [27], [30], [33], [35], [37], [39], [42] use past activities or preferences, such as the ratings on items given by users, for prediction, without using any user or product profiles. Hybrid methods [2], [3], [6], [12], [15], [16], [17], [20], [32], [34], [40], [43], [47], [48] combine both content-based methods and CF based methods by ensemble techniques.…”

Section: Introductionmentioning

confidence: 99%

Relational Collaborative Topic Regression for Recommender Systems

Wang

2015

IEEE Trans. Knowl. Data Eng.

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Abstract-Due to its successful application in recommender systems, collaborative filtering (CF) has become a hot research topic in data mining and information retrieval. In traditional CF methods, only the feedback matrix, which contains either explicit feedback (also called ratings) or implicit feedback on the items given by users, is used for training and prediction. Typically, the feedback matrix is sparse, which means that most users interact with few items. Due to this sparsity problem, traditional CF with only feedback information will suffer from unsatisfactory performance. Recently, many researchers have proposed to utilize auxiliary information, such as item content (attributes), to alleviate the data sparsity problem in CF. Collaborative topic regression (CTR) is one of these methods which has achieved promising performance by successfully integrating both feedback information and item content information. In many real applications, besides the feedback and item content information, there may exist relations (also known as networks) among the items which can be helpful for recommendation. In this paper, we develop a novel hierarchical Bayesian model called Relational Collaborative Topic Regression (RCTR), which extends CTR by seamlessly integrating the user-item feedback information, item content information, and network structure among items into the same model. Experiments on real-world datasets show that our model can achieve better prediction accuracy than the state-of-the-art methods with lower empirical training time. Moreover, RCTR can learn good interpretable latent structures which are useful for recommendation.

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Typicality-Based Collaborative Filtering Recommendation

Cited by 190 publications

References 38 publications

Fuzzy prototype classifier based on items and its application in recommender system

Fuzzy prototype classifier based on items and its application in recommender system

Inferring continuous dynamic social influence and personal preference for temporal behavior prediction

Relational Collaborative Topic Regression for Recommender Systems

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