Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Recommender systems typically leverage two types of signals to effectively recommend items to users: user activities and content matching beten user and item profiles, and recommendation models in literature are usually categorized into collaborative filtering models, content-based models and hybrid models. In practice, when rich profiles about users and items are available, and user activities are sparse (cold-start), effective content matching signals become much more important in the relevance of the recommendation. The de-facto method to measure similarity beten two pieces of text is computing the cosine similarity of the two bags of words, and each word is ighted by TF (term frequency within the document) x IDF (inverted document frequency of the word within the corpus). In general sense, TF can represent any local ighting scheme of the word within each document, and IDF can represent any global ighting scheme of the word across the corpus. ,  focus on the latter, i.e., optimizing the global term ights, for a particular recommendation domain by leveraging supervised approaches. The intuition is that some frequent words (lor IDF, e.g. ``database'') can be essential and predictive for relevant recommendation, while some rare words (higher IDF, e.g. the name of a small company) could have less predictive por. Given plenty of observed activities beten users and items as training data,  should be able to learn better domain-specific global term ights, which can further improve the relevance of recommendation.#R##N##R##N# propose a unified method that can simultaneously learn the ights of multiple content matching signals, as ll as global term ights for specific recommendation tasks. Our method is efficient to handle large-scale training data generated by production recommender systems. And experiments on LinkedIn job recommendation data justify the effectiveness of our approach.
['Yupeng Gu', 'Bo Zhao', 'David Hardtke', 'Yizhou Sun']
I am writing a research paper on a idea Learning Global Term Weights for Content-based Recommender Systems
[{"from": "human", "value": "I am writing a research paper on a idea Learning Global Term Weights for Content-based Recommender Systems"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Recommender systems typically leverage two types of signals to effectively recommend items to users: user activities and content matching beten user and item profiles, and recommendation models in literature are usually categorized into collaborative filtering models, content-based models and hybrid models. In practice, when rich profiles about users and items are available, and user activities are sparse (cold-start), effective content matching signals become much more important in the relevance of the recommendation. The de-facto method to measure similarity beten two pieces of text is computing the cosine similarity of the two bags of words, and each word is ighted by TF (term frequency within the document) x IDF (inverted document frequency of the word within the corpus). In general sense, TF can represent any local ighting scheme of the word within each document, and IDF can represent any global ighting scheme of the word across the corpus. ,  focus on the latter, i.e., optimizing the global term ights, for a particular recommendation domain by leveraging supervised approaches. The intuition is that some frequent words (lor IDF, e.g. ``database'') can be essential and predictive for relevant recommendation, while some rare words (higher IDF, e.g. the name of a small company) could have less predictive por. Given plenty of observed activities beten users and items as training data,  should be able to learn better domain-specific global term ights, which can further improve the relevance of recommendation.#R##N##R##N# propose a unified method that can simultaneously learn the ights of multiple content matching signals, as ll as global term ights for specific recommendation tasks. Our method is efficient to handle large-scale training data generated by production recommender systems. And experiments on LinkedIn job recommendation data justify the effectiveness of our approach."}]