Semantic clustering-based cross-domain recommendation

Kumar, Anil; Kumar, Nitesh; Hussain, Muzammil; Chaudhury, Santanu; Agarwal, Sumeet

doi:10.1109/cidm.2014.7008659

Cited by 26 publications

(9 citation statements)

References 14 publications

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“…The trained user and item feature vectors can also satisfy the similarity relationship between users and items on the basis of minimizing the error between the predicted rating and the actual rating. Kumar et al [ 18 ] used the Latent Direchlet Allocation (LDA) topic model [ 19 ] to model the user’s tagging information to build a user feature topic sharing space shared by different domains and then, based on this space, to find users with similar preferences in different domains and implement cross-domain recommendation.…”

Section: Related Workmentioning

confidence: 99%

Cross-Domain Recommendation Based on Sentiment Analysis and Latent Feature Mapping

Wang

et al. 2020

Entropy

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Cross-domain recommendation is a promising solution in recommendation systems by using relatively rich information from the source domain to improve the recommendation accuracy of the target domain. Most of the existing methods consider the rating information of users in different domains, the label information of users and items and the review information of users on items. However, they do not effectively use the latent sentiment information to find the accurate mapping of latent features in reviews between domains. User reviews usually include user’s subjective views, which can reflect the user’s preferences and sentiment tendencies to various attributes of the items. Therefore, in order to solve the cold-start problem in the recommendation process, this paper proposes a cross-domain recommendation algorithm (CDR-SAFM) based on sentiment analysis and latent feature mapping by combining the sentiment information implicit in user reviews in different domains. Different from previous sentiment research, this paper divides sentiment into three categories based on three-way decision ideas—namely, positive, negative and neutral—by conducting sentiment analysis on user review information. Furthermore, the Latent Dirichlet Allocation (LDA) is used to model the user’s semantic orientation to generate the latent sentiment review features. Moreover, the Multilayer Perceptron (MLP) is used to obtain the cross domain non-linear mapping function to transfer the user’s sentiment review features. Finally, this paper proves the effectiveness of the proposed CDR-SAFM framework by comparing it with existing recommendation algorithms in a cross-domain scenario on the Amazon dataset.

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Section: Related Workmentioning

confidence: 99%

Cross-Domain Recommendation Based on Sentiment Analysis and Latent Feature Mapping

Wang

et al. 2020

Entropy

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“…[10] used textual information of items to map them across domains. These mappings were then used to give cross-domain recommendations.…”

Section: Related Workmentioning

confidence: 99%

“…The work described in [6] stated that identity resolution solutions can be used by various applications, such as security, privacy and recommendation systems. Some research efforts in recommendation systems concentrate on recommendations across domains, e.g., [8][9][10]. However, these cross-domain recommendation systems focus solely on matching items and have not considered users' preferences across available platforms.…”

Section: Introductionmentioning

confidence: 99%

Making recommendations by integrating information from multiple social networks

2016

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It is becoming a common practice to use recommendation systems to serve users of web-based platforms such as social networking platforms, review web-sites, and e-commerce web-sites. Each platform produces recommendations by capturing, maintaining and analyzing data related to its users and their behavior. However, people generally use different web-based platforms for different purposes. Thus, each platform captures its own data which may reflect certain aspects related to its users. Integrating data from multiple platforms may widen the perspective of the analysis and may help in modeling users more effectively. Motivated by this, we developed a recommendation framework which integrates data collected from multiple platforms. For this purpose, we collected and anonymized datasets which contain information from several social networking and social media platforms, namely BlogCatalog, Twitter, Flickr, Facebook, YouTube and LastFm. The collected and integrated data forms a consolidated repository that may become a valuable source for researchers and practitioners. We implemented a number of recommendation methodologies to observe their performance for various cases which involve using single versus multiple features from a single source versus multiple sources. The conducted

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“…In [4] the authors have determined semantic relationships between non-identical, but possibly semantically equivalent, words in multiple domain vocabularies, so as to capture relationships across information obtained in distinct domains. They have used WordNet ontology to measure the semantic relation between the textual words.…”

Section: Related Workmentioning

confidence: 99%

A Novel Approach to Cluster Web Pages Dynamically based on Domain Knowledge

D’abreo¹,

Khandare²,

Janrao³

2016

IJAIS

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Web Pages which are recommended by the normal web page recommendation system are listed and are not clustered. The web search is based on keyword. The search engine does not understand the meaning of the searched query as it does not have a background domain knowledge of the searched query. The earlier search engine designed clustered the web pages according to static clusters formed [2]. As static clustering, faced some drawbacks of mapping the Web pages, there was a need to find the solution for the same. This paper presents a solution to form the clusters dynamically considering the domains for efficient clustering.

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Semantic clustering-based cross-domain recommendation

Cited by 26 publications

References 14 publications

Cross-Domain Recommendation Based on Sentiment Analysis and Latent Feature Mapping

Cross-Domain Recommendation Based on Sentiment Analysis and Latent Feature Mapping

Making recommendations by integrating information from multiple social networks

A Novel Approach to Cluster Web Pages Dynamically based on Domain Knowledge

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