Location and Activity Recommendation by Using Consecutive Itinerary Matching Model

Location and Activity Recommendation by Using Consecutive Itinerary Matching Model 劉俊賢 Jiun-Shian Liu 國立成功大學資訊工程學系 Department of Computer Science and Information Engineering National Cheng Kung University 盧文祥 Wen-Hsiang Lu 國立成功大學資訊工程學系 Department of Computer Science and Information Engineering National Cheng Kung University whlu@ncku.edu.tw 摘要 許多人都有過這樣的經驗，因為事前未詳細規劃旅遊行程而導致不知道該選擇何處或何 種活動做為下一步行程。本研究提出連續行程媒合模型(CIMM)，針對行動裝置使用者 當下最新的打卡資訊，找出使用者下一步的地點及活動需求。雖然我們提出的 CIMM 在初步的實驗指得到的 30%的 top-1 inclusion rate，但是我們利用打卡資訊探勘連續行 程，然後推薦給行動裝置使用者及時的下一步地點及活動需求卻是一項創新技術。 Abstract In fact, most people have had the experience that they haven’t made detailed itinerary in advance before a journey, and as a result they don’t know what place or what kind of activity is suitable as the next visit location and activity after they engage in an activity in a certain place. To alleviate such problem, in this paper, we proposed the Consecutive Itinerary Matching Model to help mobile users find next locations and activities in line with their leisure needs. This model effectively utilizes time, location, user, and activity as features to find the most possible “Consecutive Itinerary” and then recommend mobile users next locations and activities. In this preliminary study, although our approach achieved only about 30% top-1 inclusion rate, however, to our knowledge, this work is novel for the recommendation of location and activity based on consecutive itinerary discovery from check-in data. 關鍵詞：地點推薦，活動推薦 Keywords: Location Recommendation, Activity Recommendation 1. Introduction In fact, most people have had the experience that they haven’t made detailed itinerary in advance before a journey, and as a result they don’t know what place or what kind of activity is suitable as the next visit location and activity after they engage in an activity in a certain Proceedings of the Twenty-Fifth Conference on Computational Linguistics and Speech Processing (ROCLING 2013) 288 place. To alleviate such problem, in this paper, we intend to propose an effective method to help mobile users find next locations and activities in line with their leisure needs. For example, after somebodies watches a film in the cinema, we can recommend them to go bowling next. In the last few years, when people go places, the common thing for them to do is that using Facebook to check in to the places and let their friends know exactly where they are and what they’re doing. Check-in data is a new and useful resource for our work of finding next locations and activities for mobile users. In addition, many bloggers describe travel itineraries in their blog articles which are really worth discovering. Thus, to recommend effective next potential location-activity pair to mobile users, our idea is to utilize these two kinds of resources, check-in data and travel blogs. In this study, we collect and analyze a large number of travel itineraries from these two resources, and then use these data to train the Consecutive Itinerary Matching Model (CIMM). This model uses time, location, activity, and user information in check-ins and travel blogs as features to find the most possible consecutive itinerary associated to a user’s current location and activity, and then recommends him next locations and activities. The example of recommending mobile users next location and activity based …

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