Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Venue recommendation aims to assist users by making personalised#R##N#suggestions of venues to visit, building upon data available from#R##N#location-based social networks (LBSNs) such as Foursquare. A#R##N#particular challenge for this task is context-aware venue recommendation#R##N#(CAVR), which additionally takes the surrounding context of#R##N#the user (e.g. the user’s location and the time of day) into account#R##N#in order to provide more relevant venue suggestions. To address the#R##N#challenges of CAVR,  describe two approaches that exploit word#R##N#embedding techniques to infer the vector-space representations of#R##N#venues, users’ existing preferences, and users’ contextual preferences.#R##N#Our evaluation upon the test collection of the TREC 2015#R##N#Contextual Suggestion track demonstrates that  can significantly#R##N#enhance the effectiveness of a state-of-the-art venue recommendation#R##N#approach, as ll as produce context-aware recommendations#R##N#that are at least as effective as the top TREC 2015 systems.
['Jarana Manotumruksa', 'Craig Macdonald', 'Iadh Ounis']
I am writing a research paper on a idea Modelling User Preferences using Word Embeddings for Context-Aware Venue Recommendation
[{"from": "human", "value": "I am writing a research paper on a idea Modelling User Preferences using Word Embeddings for Context-Aware Venue Recommendation"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Venue recommendation aims to assist users by making personalised#R##N#suggestions of venues to visit, building upon data available from#R##N#location-based social networks (LBSNs) such as Foursquare. A#R##N#particular challenge for this task is context-aware venue recommendation#R##N#(CAVR), which additionally takes the surrounding context of#R##N#the user (e.g. the user\u2019s location and the time of day) into account#R##N#in order to provide more relevant venue suggestions. To address the#R##N#challenges of CAVR,  describe two approaches that exploit word#R##N#embedding techniques to infer the vector-space representations of#R##N#venues, users\u2019 existing preferences, and users\u2019 contextual preferences.#R##N#Our evaluation upon the test collection of the TREC 2015#R##N#Contextual Suggestion track demonstrates that  can significantly#R##N#enhance the effectiveness of a state-of-the-art venue recommendation#R##N#approach, as ll as produce context-aware recommendations#R##N#that are at least as effective as the top TREC 2015 systems."}]