An approach for combining content-based and collaborative filters

Li, Qing; Kim, Byeong Man

doi:10.3115/1118935.1118938

Cited by 59 publications

(29 citation statements)

References 21 publications

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“…Nandi and H. V. Jagdish [17] demonstrates a different query interface that allows users to build a rich search query with no any prior knowledge of the fundamental system or data. In this they use the structure information to autocomplete characters or content names in query forms.…”

Section: Query Formsmentioning

confidence: 99%

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Annotation Approach for Document with Recommendation

Channe¹,

Dhakulkar²

2016

IJMPICT

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Section: Query Formsmentioning

confidence: 99%

“…Authors D. Yin et al [17] address the difficulty of tag prediction by recommending a probabilistic model for personalized tag prediction.…”

Section: Probabilistic Modelmentioning

confidence: 99%

Annotation Approach for Document with Recommendation

Channe¹,

Dhakulkar²

2016

IJMPICT

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“…This also brings about another related problem, which is the inability to capture the relationship between two similar items that have never been rated by the same user. In this case, these two items are not considered alike [21].…”

Section: A Recommender Systems Techniquesmentioning

confidence: 99%

“…Indeed, one of the most important advantages that CF has over contentbased filtering is the potential for generating serendipitous recommendations [2]. However, combining the two RS techniques, CF and CBF, have shown to solve some of the drawbacks of both techniques [21], [22], [24]. Besides, content information of items usually contains valuable information; hence it makes it desirable to include attribute information in CF models, the so called hybrid collaborative/content-based Filtering methods.…”

Section: Content-based Filteringmentioning

confidence: 99%

Empirical Analysis of Attribute-Aware Recommender System Algorithms Using Synthetic Data

Tso¹,

Schmidt-Thieme²

2006

JCP

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Abstract-As the amount of online shoppers grows rapidly, the need of recommender systems for e-commerce sites are demanding, especially when the number of users and products being offered online continues to increase dramatically. There have been many ongoing researches on recommender systems and in investigating recommendation algorithms that could optimize the recommendation quality. However, adequate and public datasets of users and products have always been demanding to better evaluate recommender system algorithms. Yet, the amount of public data, especially data containing adequate content information (attributes) is limited. When evaluating recommendation algorithms, it is important to observe the behavior of the algorithm as the characteristic of data varies. Synthetic data would allow the application of systematic changes on the data which cannot be done with real-life data. Although studies on synthetic data for the use of recommender systems have been investigated, artificial data with attributes information are rarely looked into. In this paper, we review public and synthetic data that are applied in the field of recommender systems. A synthetic data generation methodology that considers attributes will also be discussed. Furthermore, we present empirical evaluations on existing attributeaware recommendation algorithms and other state-of-theart algorithms using real-life dataset as well as variable synthetic data to observe their behavior as the characteristic of data varies. In particular, the informativeness of attributes is being further investigated with both real-life datasets with augmented attributes sets as well as synthetic datasets with attributes. We have shown that a reasonably good overview of the behavior of attribute-aware algorithms can be obtained by using synthetic data compared to results done with real-life datasets.

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“…These methods perform standard user-or item-based CF on top of the rating matrix enriched by pseudo-items or -users. (iii)a) Methods that combine linearly the predictions of a pure CBF model and a pure CF model (Claypool et al (1999), Pazzani (1999), Good et al (1999), Li and Kim (2003))…”

Section: Related Workmentioning

confidence: 99%

Sensitivity of Attributes on the Performance of Attribute-Aware Collaborative Filtering

Tso

Schmidt-Thierne

Studies in Classification, Data Analysis, and Knowledge Organization

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Abstract. Collaborative Filtering (CF), the most commonly-used technique for recommender systems, does not make use of object attributes. Several hybrid recommender systems have been proposed, that aim at improving the recommendation quality by incorporating attributes in a CF model.In this paper, we conduct an empirical study of the sensitivity of attributes for several existing hybrid techniques using a movie dataset with an augmented movie attribute set. In addition, we propose two attribute selection measures to select informative attributes for attribute-aware CF filtering algorithms.

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An approach for combining content-based and collaborative filters

Cited by 59 publications

References 21 publications

Annotation Approach for Document with Recommendation

Annotation Approach for Document with Recommendation

Empirical Analysis of Attribute-Aware Recommender System Algorithms Using Synthetic Data

Sensitivity of Attributes on the Performance of Attribute-Aware Collaborative Filtering

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