G.4. Information Storage and Retrieval
V. Derhami; J. Paksima; H. Khajeh
Abstract
Principal aim of a search engine is to provide the sorted results according to user’s requirements. To achieve this aim, it employs ranking methods to rank the web documents based on their significance and relevance to user query. The novelty of this paper is to provide user feedback-based ranking ...
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Principal aim of a search engine is to provide the sorted results according to user’s requirements. To achieve this aim, it employs ranking methods to rank the web documents based on their significance and relevance to user query. The novelty of this paper is to provide user feedback-based ranking algorithm using reinforcement learning. The proposed algorithm is called RRLUFF, in which the ranking system is considered as the agent of the learning system and the selection of documents is displayed to the user as the agent's action. Reinforcement signal in this system is calculated based on user's click on the documents. Action-values in the RRLUFF algorithm are calculated for each feature of the document-query pair. In RRLUFF method, each feature is scored based on the number of the documents related to the query and their position in the ranked list of that feature. For learning, documents are sorted according to modified scores for the next query. Then, according to the position of a document in the ranking list, some documents are selected based on the random distribution of their scores to display to the user. OHSUMED and DOTIR benchmark datasets are used to evaluate the proposed method. The evaluation results indicate that the proposed method is more effective than the related methods in terms of P@n, NDCG@n, MAP, and NWN.
G.4. Information Storage and Retrieval
V. Derhami; J. Paksima; H. Khajah
Abstract
The main challenge of a search engine is ranking web documents to provide the best response to a user`s query. Despite the huge number of the extracted results for user`s query, only a small number of the first results are examined by users; therefore, the insertion of the related results in the first ...
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The main challenge of a search engine is ranking web documents to provide the best response to a user`s query. Despite the huge number of the extracted results for user`s query, only a small number of the first results are examined by users; therefore, the insertion of the related results in the first ranks is of great importance. In this paper, a ranking algorithm based on the reinforcement learning and user`s feedback called RL3F are considered. In the proposed algorithm, the ranking system has been considered to be the agent of learning system and selecting documents to display to the user is as the agents’ action. The reinforcement signal in the system is calculated according to a user`s clicks on documents. Action-value values of the proposed algorithm are computed for each feature. In each learning cycle, the documents are sorted out for the next query, and according to the document in the ranked list, documents are selected at random to show the user. Learning process continues until the training is completed. LETOR3 benchmark is used to evaluate the proposed method. Evaluation results indicated that the proposed method is more effective than other methods mentioned for comparison in this paper. The superiority of the proposed algorithm is using several features of document and user`s feedback simultaneously.
