摘要
如何对混杂无序的信息进行筛选和过滤,并将用户最关注和最感兴趣的信息进行展现,成为信息爆炸时代最重要的挑战性问题之一。在此背景下,推荐系统应运而生,它不像搜索引擎一样仅对用户提供的显式需求进行被动匹配,而是可以根据用户潜在的兴趣和爱好主动进行信息(项目)推荐,因此可以最大程度上提升用户体验、提高服务质量。然而,已有推荐算法经常受用户兴趣过拟合和用户冷启动等问题的困扰,导致了推荐效果的不理想。为此,本文提出对推荐系统中的用户进行兴趣建模,准确理解用户当前情境下的需求,然后,设计高效的推荐算法,以提高用户满意度和商家收益。本文的工作与贡献可以概括如下：
首先,提出了基于概率主题模型的用户兴趣表示方法,构建了基于随机游走的兴趣扩展模型。当前协同过滤推荐算法往往只关注用户和系统的交互信息,缺乏对用户兴趣深度理解。针对该问题,本文探索了用户和项目之间存在的隐式兴趣层。具体而言,设计了用户-兴趣-项目的三层推荐表示模型,在利用概率主题模型获取用户当前兴趣后,提出基于随机游走的用户兴趣扩展与兴趣传播算法,并将符合用户兴趣的项目进行推荐。基于此,构建了一个面向项目、基于模型的协同过滤推荐算法iExpand。在三个标准数据集上的大量实验结果表明,iExpand生成的推荐列表可以更准确地把握用户当前兴趣。
其次,提出了情境丰富时的游客兴趣建模方法,设计了Cocktail旅游套餐个性化推荐算法。针对移动推荐的情境感知特性和数据稀疏等挑战,对游客的旅游行为数据进行了细致分析,发现了旅游套餐中的旅游景点之间隐含的时空关联性。基于此发现,提出融合情境信息的游客兴趣表示方法TAST,将具有相似旅游偏好(如旅游季节和地点)的游客映射到相近的隐空间,实现游客兴趣和套餐内容的可比较性。基于该兴趣模型和套餐价格约束等,设计实现了Cocktail旅游套餐推荐算法,为游客进行个性化的旅游套餐推荐服务。在一个旅游公司十年真实数据上的实验结果表明,与当前具有代表性的推荐算法相比,该系统显著提高了推荐精度。
最后,提出对新用户进行兴趣引导的最优初始项目推荐算法。针对如何从商家收益的角度对新用户进行初始项目推荐的问题,研究了网络中的最有影响力结点(项目)识别方法。首先,提出项目消费网络中的最优项目启发式选择算法,为新用户推荐最有影响力的项目,从而诱导他们消费更多的项目；其次,提出基于PageRank线性信息传播模型的最优初始项目识别算法,给定用户的已有消费项目集合(已有兴趣),利用贪心策略寻找能够带来最多潜在消费的一组项目进行推荐,结合商家潜在收益,对推荐的有效性进行评价。
One of the most important challenges in the era of information explosion is the way to filter out the disordered mixed information and then select the part that a given user may like. Along this line, recommender systems were proposed. Unlike search engines, which extract and return information according to user queries, recommender systems could automatically make item recommendations by mining user latent interests without user interventions. Thus, this technique has been successfully applied for improving the quality of services in a number of fields. However, existing recommendation algorithms often suffer from user interest overspecialization problem and cold-start problem. To that end, in this paper, we make a focused study of designing and applying recommenders based on user interests modeling for further enhancing both user experience and profit of the system. Our contributions could be summarized as:
Firstly, we propose a method to represent user interests in collaborative filter-ing, and then describe a user interests expansion model by personalized ranking. Existing collaborative filtering based recommender systems usually focus on ex-ploiting the information about the user's interaction with the systems, and the information about latent user interests is largely under-explored. Since learning to leverage the information about user interests is often critical for making better recommendations, we introduce a three-layer, user-interests-item, representation scheme. Specifically, after a topic model based method is used to capture each user's interests, personalized ranking is developed for predicting user's possible interests expansion. Moreover, a diverse recommendation list is generated by us-ing user latent interests as an intermediate layer between users and items. This recommendation strategy is summarized into an item-oriented model-based col-laborative framework, named iExpand. The extensive experimental results show that iExpand can lead to better ranking performance than state-of-the-arts.
Secondly, we propose a way for context-rich tourist interest modeling, and design a cocktail approach (Cocktail) on personalized travel package recommen-dation. To deal with the technical and domain challenges in travel recommenda-tion, we first analyze the unique characteristics of travel packages and discover the spatial-temporal autocorrelations among their landscapes. Then develop the TAST model for representing the interests of the tourists, which can extract the interests conditioned on both the tourists and the intrinsic features (i.e., loca-tions, travel seasons) of the landscapes. Based on this TAST model, Cocktail is developed for personalized travel package recommendation by considering some additional factors. Finally, we evaluate TAST model and Cocktail on real-world travel package data (last for ten years) provided by a travel company in Chi-na. The experimental results show that the TAST model can effectively capture the unique characteristics of the travel data and the Cocktail is thus much more effective than traditional methods for travel package recommendation.
Lastly, we provide an idea of recommending seed items to cold-start users for user interest elicitation. For choosing and evaluating seed items from a systematic perspective, we propose to identify influential seed nodes(items) from the item consumption network. Specifically, we first provide several influential seed items selection heuristics, which recommend the most influential items that can bring in more consumptions; Then, we present a seed items selection algorithm based on a linear information propagation model similar to PageRank. Different from the previous heuristics which do not consider the possible influence overlaps between seed items and select all the items simultaneously, the linear algorithm identifies the independent influence of each item and selects the seeds one by one following a greedy strategy. Finally, we show the effectiveness of these methods by the expected number of consumptions of cold-start users.
引文
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