Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: In information retrieval, pseudo-relevance feedback (PRF) refers to a strategy for updating the query model using the top retrieved documents. PRF has been proven to be highly effective in improving the retrieval performance. ,  look at the PRF task as a recommendation problem: the goal is to recommend a number of terms for a given query along with ights, such that the final ights of terms in the updated query model better reflect the terms' contributions in the query. To do so,  propose RFMF, a PRF framework based on matrix factorization which is a state-of-the-art technique in collaborative recommender systems. Our purpose is to predict the ight of terms that have not appeared in the query and matrix factorization techniques are used to predict these ights. In RFMF,  first create a matrix whose elements are computed using a ight function that shows how much a term discriminates the query or the top retrieved documents from the collection. Then,  re-estimate the created matrix using a matrix factorization technique. Finally, the query model is updated using the re-estimated matrix. RFMF is a general framework that can be employed with any retrieval model. ,  implement this framework for two widely used document retrieval frameworks: language modeling and the vector space model. Extensive experiments over several TREC collections demonstrate that the RFMF framework significantly outperforms competitive baselines. These results indicate the potential of using other recommendation techniques in this task.
['Hamed Zamani', 'Javid Dadashkarimi', 'Azadeh Shakery', 'W. Bruce Croft']
I am writing a research paper on a idea Pseudo-Relevance Feedback Based on Matrix Factorization
[{"from": "human", "value": "I am writing a research paper on a idea Pseudo-Relevance Feedback Based on Matrix Factorization"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: In information retrieval, pseudo-relevance feedback (PRF) refers to a strategy for updating the query model using the top retrieved documents. PRF has been proven to be highly effective in improving the retrieval performance. ,  look at the PRF task as a recommendation problem: the goal is to recommend a number of terms for a given query along with ights, such that the final ights of terms in the updated query model better reflect the terms' contributions in the query. To do so,  propose RFMF, a PRF framework based on matrix factorization which is a state-of-the-art technique in collaborative recommender systems. Our purpose is to predict the ight of terms that have not appeared in the query and matrix factorization techniques are used to predict these ights. In RFMF,  first create a matrix whose elements are computed using a ight function that shows how much a term discriminates the query or the top retrieved documents from the collection. Then,  re-estimate the created matrix using a matrix factorization technique. Finally, the query model is updated using the re-estimated matrix. RFMF is a general framework that can be employed with any retrieval model. ,  implement this framework for two widely used document retrieval frameworks: language modeling and the vector space model. Extensive experiments over several TREC collections demonstrate that the RFMF framework significantly outperforms competitive baselines. These results indicate the potential of using other recommendation techniques in this task."}]