Research Resources for Recommender Systems
نویسندگان
چکیده
The recommender system research community has become a well-identified and active research community built around the goal of understanding, building, and improving recommender systems. A recurring theme since the first workshop in Berkeley (March 1996) has been the development of substantial research infrastructure—typically publicly-accessible recommendation services—as a tool to conduct research. The result has been a variety of innovative recommender systems, including the Bellcore Video Recommender, M.I.T.’s Ringo system, Stanford’s Fab, and our own GroupLens project. This plethora of systems helped the field achieve wider visibility, particularly through Varian and Resnick’s special issue of Communications of the ACM (March, 1997), and has spawned several commercial recommender system projects and companies.
منابع مشابه
Context-Aware Recommender Systems: A Review of the Structure Research
Recommender systems are a branch of retrieval systems and information matching, which through identifying the interests and requires of the user, help the users achieve the desired information or service through a massive selection of choices. In recent years, the recommender systems apply describing information in the terms of the user, such as location, time, and task, in order to produce re...
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Introduction: Today, researchers prefer to have most of their required information at their fingertips. Scholarly or research paper recommender systems are intelligent systems that aim to recommend the most appropriate scientific papers or resources based on users' needs. Past studies have shown that contextual information such as users', system' and environment' contexts influence the quality ...
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The rapid development of technology, the Internet, and the development of electronic commerce have led to the emergence of recommender systems. These systems will assist the users in finding and selecting their desired items. The accuracy of the advice in recommender systems is one of the main challenges of these systems. Regarding the fuzzy systems capabilities in determining the borders of us...
متن کاملIncreasing the Accuracy of Recommender Systems Using the Combination of K-Means and Differential Evolution Algorithms
Recommender systems are the systems that try to make recommendations to each user based on performance, personal tastes, user behaviors, and the context that match their personal preferences and help them in the decision-making process. One of the most important subjects regarding these systems is to increase the system accuracy which means how much the recommendations are close to the user int...
متن کاملAn Effective Algorithm in a Recommender System Based on a Combination of Imperialist Competitive and Firey Algorithms
With the rapid expansion of the information on the Internet, recommender systems play an important role in terms of trade and research. Recommender systems try to guess the user's way of thinking, using the in-formation of user's behavior or similar users and their views, to discover and then propose a product which is the most appropriate and closest product of user's interest. In the past dec...
متن کاملEvaluation of recommender systems: A multi-criteria decision making approach
The evaluation and selection of recommender systems is a difficult decision making process. This difficulty is partially due to the large diversity of published evaluation criteria in addition to lack of standardized methods of evaluation. As such, a systematic methodology is needed that explicitly considers multiple, possibly conflicting metrics and assists decision makers to evaluate and find...
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