Estimating Probabilities in Recommendation Systems

Sun, Mingxuan; Lebanon, Guy; Kidwell, Paul

doi:10.1111/j.1467-9876.2011.01027.x

Cited by 23 publications

(23 citation statements)

References 16 publications

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“…This lack of information in item-consumption histories is referred to as the sparsity problem, and it is one of the most limiting factors for its performance in practice. Many techniques, ranging from dimension reduction to sparse data smoothing, were proposed to address this issue [4,[21][22][23][24].…”

Section: Related Workmentioning

confidence: 99%

Improved Neighborhood Search for Collaborative Filtering

Chung¹,

Kim²,

Park³

et al. 2018

IJFIS

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k-Nearest Neighbor (k-NN) and other user-based collaborative filtering (CF) algorithms have gained popularity because of the simplicity of their algorithms and performance. As the performance of such algorithms largely depends on neighborhood selection, it is important to select the most suitable neighborhood for each active user. Previous user-based CF simply relies on similar users or common experts in this regard; however, because users have different tastes as well as different expectations for expert advice, similar users or common experts may not always be the best neighborhood for CF. In search of a more suitable neighborhood, so-called personalized experts develop personalized expert features. Through experimentation, we show that personalized experts are different from similar users, common experts, or similar common experts. The personalized, expert-based CF algorithm outperforms k-NN and other user-based CF algorithms.

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Section: Related Workmentioning

confidence: 99%

Improved Neighborhood Search for Collaborative Filtering

Chung¹,

Kim²,

Park³

et al. 2018

IJFIS

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show abstract

“…User features may include demographic information, location, activity context, and device capability. Collaborative filtering goes beyond content-based methods to correlate users and items based on the assumption that users prefer items favored by the like-minded [1,5,[13][14][15][16][17][18][19].…”

Section: Related Workmentioning

confidence: 99%

A Multi-Modality Deep Network for Cold-Start Recommendation

Sun

Zhang

2018

BDCC

Self Cite

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Collaborative filtering (CF) approaches, which provide recommendations based on ratings or purchase history, perform well for users and items with sufficient interactions. However, CF approaches suffer from the cold-start problem for users and items with few ratings. Hybrid recommender systems that combine collaborative filtering and content-based approaches have been proved as an effective way to alleviate the cold-start issue. Integrating contents from multiple heterogeneous data sources such as reviews and product images is challenging for two reasons. Firstly, mapping contents in different modalities from the original feature space to a joint lower-dimensional space is difficult since they have intrinsically different characteristics and statistical properties, such as sparse texts and dense images. Secondly, most algorithms only use content features as the prior knowledge to improve the estimation of user and item profiles but the ratings do not directly provide feedback to guide feature extraction. To tackle these challenges, we propose a tightly-coupled deep network model for fusing heterogeneous modalities, to avoid tedious feature extraction in specific domains, and to enable two-way information propagation from both content and rating information. Experiments on large-scale Amazon product data in book and movie domains demonstrate the effectiveness of the proposed model for cold-start recommendation.

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“…Statistical translation has been used in information retrieval (IR) for query expansion [21] and recommendation systems [45]. In our paper, translation models are mainly used to get richer profiles for the KR concepts.…”

Section: Statistical Language Modelsmentioning

confidence: 99%

“…When k → ∞ we obtain the eigen-based smoothing of T G , which has been widely adopted for document classification and spectral clustering [45]. In this paper, we will use these kernels only for smoothing semantic query models across concept taxonomies.…”

Section: Statistical Language Modelsmentioning

confidence: 99%

Tailored Semantic Annotation for Semantic Search

2015

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This paper presents a novel method for semantic annotation and search of a target corpus using several knowledge resources (KRs). This method relies on a formal statistical framework in which KR concepts and corpus documents are homogeneously represented using statistical language models. Under this framework, we can perform all the necessary operations for an efficient and effective semantic annotation of the corpus. Firstly, we propose a coarse tailoring of the KRs w.r.t the target corpus with the main goal of reducing the ambiguity of the annotations and their computational overhead. Then, we propose the generation of concept profiles, which allow measuring the semantic overlap of the KRs as well as performing a finer tailoring of them. Finally, we propose how to semantically represent documents and queries in terms of the KRs concepts and the statistical framework to perform semantic search. Experiments have been carried out with a corpus about web resources which includes several Life Sciences catalogues and Wikipedia pages related to web resources in general (e.g., databases, tools, services, etc). Results demonstrate that the proposed method is more effective and efficient than state-ofthe-art methods relying on either context-free annotation or keyword-based search.

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Estimating Probabilities in Recommendation Systems

Cited by 23 publications

References 16 publications

Improved Neighborhood Search for Collaborative Filtering

Improved Neighborhood Search for Collaborative Filtering

A Multi-Modality Deep Network for Cold-Start Recommendation

Tailored Semantic Annotation for Semantic Search

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