Probabilistic Social Sequential Model for Tour Recommendation
Probabilistic Social Sequential Model for Tour Recommendation
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DOI：
10.1145/3018661.3018711
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发表时间：
2017-02
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影响因子：
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通讯作者：
Vineeth Rakesh;Niranjan Jadhav;Alexander Kotov;Chandan K. Reddy
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文献类型：
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作者：
Vineeth Rakesh;Niranjan Jadhav;Alexander Kotov;Chandan K. Reddy
基于位置的服务（如Foursquare和Yelp）的普遍增长使研究人员能够通过利用大量旅行者留下的地理时间面包屑来将更好的个性化融入推荐模型。在本文中，我们探讨了旅游路径推荐，这是智能城市导航的应用之一，旨在推荐兴趣点（POI）序列的游客。目前，旅行者依赖于搜索网络、浏览诸如Trip Advisor之类的网站以及阅读旅游博客的繁琐且耗时的过程来编制行程。另一方面，那些没有提前计划行程的人发现很难实时做到这一点，因为没有自动化系统可以为旅行者提供个性化的行程。为了解决这个问题，我们提出了一个旅游推荐模型，使用概率生成框架，将用户的分类偏好，从他们的社交圈的影响，动态的旅游转换（或模式）和场馆的受欢迎程度推荐的POI序列的游客。通过对Foursquare丰富的旅行模式数据集进行全面的实验，我们证明了我们的模型能够通过为旅行者提供上下文和有意义的推荐来超越最先进的概率旅游推荐模型。
The pervasive growth of location-based services such as Foursquare and Yelp has enabled researchers to incorpo- rate better personalization into recommendation models by leveraging the geo-temporal breadcrumbs left by a plethora of travelers. In this paper, we explore Travel path recommendation, which is one of the applications of intelligent urban navigation that aims in recommending sequence of point of interest (POIs) to tourists. Currently, travelers rely on a tedious and time-consuming process of searching the web, browsing through websites such as Trip Advisor, and reading travel blogs to compile an itinerary. On the other hand, people who do not plan ahead of their trip find it extremely difficult to do this in real-time since there are no automated systems that can provide personalized itinerary for travelers. To tackle this problem, we propose a tour recommendation model that uses a probabilistic generative framework to incorporate user's categorical preference, influence from their social circle, the dynamic travel transitions (or patterns) and the popularity of venues to recommend sequence of POIs for tourists. Through comprehensive experiments over a rich dataset of travel patterns from Foursquare, we show that our model is capable of outperforming the state-of-the-art probabilistic tour recommendation model by providing contextual and meaningful recommendation for travelers.