Recommending Curated Content Using Implicit Feedback
نویسندگان
چکیده
منابع مشابه
Using Implicit Relevance Feedback in
The explosive growth of information on the World Wide Web demands eeective intelligent search and ltering methods. Consequently, techniques have been developed that extract conceptual information from documents to build domain models automatically. The model we build is a taxonomy of conceptual terms that is used in a search assistant to help the user navigate to the right set of required docum...
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This paper describes Pinview, a contentbased image retrieval system that exploits implicit relevance feedback during a search session. The goal is to retrieve interesting images and the relevance feedback could be eye movements or clicks on the images. Pinview contains several novel methods that infer the intent of the user. From relevance feedback and visual features of images Pinview learns a...
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The popularity of social media creates a large amount of user-generated content, playing an important role in addressing cold-start problems in recommendation. Although much effort has been devoted to incorporating this information into recommendation, past work mainly targets explicit feedback. There is still no general framework tailored to implicit feedback, such as views, listens, or visits...
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Recommender systems have explored a range of implicit feedback approaches to capture users’ current interests and preferences without intervention of users’ work. However, the problem of implicit feedback elicit negative feedback, because users mainly target information they want. Therefore, there have been few studies to test how effective negative implicit feedback is to personalize informati...
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ژورنال
عنوان ژورنال: Asian Journal of Research in Computer Science
سال: 2020
ISSN: 2581-8260
DOI: 10.9734/ajrcos/2020/v5i230130