A novel mobile recommender system for indoor shopping

With the widespread usage of mobile terminals, the mobile recommender system is proposed to improve recommendation performance, using positioning technologies. However, due to restrictions of existing positioning technologies, mobile recommender systems are still not being applied to indoor shopping, which continues to be the main shopping mode. In this paper, we develop a mobile recommender system for stores under the circumstance of indoor shopping, based on the proposed novel indoor mobile positioning approach by using received signal patterns of mobile phones, which can overcome the disadvantages of existing positioning technologies. Especially, the mobile recommender system can implicitly capture users’ preferences by analyzing users’ positions, without requiring users’ explicit inputting, and take the contextual information into consideration when making recommendations. A comprehensive experimental evaluation shows the new proposed mobile recommender system achieves much better user satisfaction than the benchmark method, without losing obvious recommendation performances.     

A new hybrid recommendation algorithm with privacy

Providing accurate and dependable recommendations efficiently while preserving privacy is essential for e‐commerce sites to recruit new customers and keep the existing ones. Such sites might be able to increase their sales and profits while customers can obtain precise and trustworthy predictions if they use appropriate collaborative filtering (CF) algorithms without deeply jeopardizing users' privacy. We propose a new recommendation algorithm, which is a hybrid‐memory and model‐based algorithm to generate truthful referrals efficiently. Moreover, we use randomization techniques to preserve users' privacy while still offering CF services with decent accuracy. We perform real data‐based trials and analyse our proposed schemes in terms of privacy, accuracy, and performance.     

一种融合项目特征和移动用户信任关系的推荐算法

协同过滤推荐系统中普遍存在评分数据稀疏问题.传统的协同过滤推荐系统中的余弦、Pearson 等方法都是基于共同评分项目来计算用户间的相似度;而在稀疏的评分数据中,用户间共同评分的项目所占比重较小,不能准确地找到偏好相似的用户,从而影响协同过滤推荐的准确度.为了改变基于共同评分项目的用户相似度计算,使用推土机距离（earth mover's distance,简称EMD）实现跨项目的移动用户相似度计算,提出了一种融合项目特征和移动用户信任关系的协同过滤推荐算法.实验结果表明：与余弦、Pearson 方法相比,融合项目特征的用户相似度计算方法能够缓解评分数据稀疏对协同过滤算法的影响.所提出的推荐算法能够提高移动推荐的准确度.     

融合用户信任模型的协同过滤推荐算法

协同过滤推荐是电子商务系统中最为重要的技术之一.随着电子商务系统中用户数目和商品数目的增加,用户-项目评分数据稀疏性问题日益显著.传统的相似度度量方法是基于用户共同评分项目计算的,而过于稀疏的评分使得不能准确预测用户偏好,导致推荐质量急剧下降.针对上述问题,本文考虑用户评分相似性和用户之间信任关系对推荐结果的影响,利用层次分析法实现用户信任模型的构建,提出一种融合用户信任模型的协同过滤推荐算法.实验结果表明： 该算法能够有效反映用户认知变化,缓解评分数据稀疏性对协同过滤推荐算法的影响,提高推荐结果的准确度.     

Integrating Collaborative and Reclusive Methods for Effective Recommendations: A Fuzzy Bayesian Approach

Collaborative filtering (CF), the most successful and widely used technique, recommends items based on the preferences of similar users. The main potentials of CF are its cross‐genre recommendation ability, and that it is completely independent of representation of the items being recommended. However, CF suffers from sparsity and cold start problems. On the other hand, a highly effective variant of content‐based filtering (CBF), reclusive methods (RMs) based on the preference of the single individual for whom recommendations to be made, provides a methodology that considers uncertainty and the multivalued nature of item features as well as user preferences in a content‐based framework using fuzzy logic approaches. The adoption of RM paradigm has several advantages when compared to CF such as sparsity and new item problem, but it suffers from overspecialization and limited content analysis. In view of the complementary nature of CF and RM, we develop a hybrid recommender system (RS) that helps in alleviating aforementioned problems in each approach. First, we propose fuzzy naïve Bayesian classifier based CF (FNB‐CF) and RM (FNB‐RM) for handling correlation‐based similarity problems. To overcome individual weaknesses of FNB‐CF and FNB‐RM, we develop a hybrid RS, FNB‐CF‐RM. Effectiveness of our proposed hybrid RS is demonstrated through experimental results using the MovieLens and IMDb data sets.     

移动电话内容服务系统的个性化推荐

移动电话内容服务系统允许移动用户通过移动互联技术浏览、购买和下载系统内容，是当前移动增值领域研究的热点。具有较强的时空灵活性，但在信息浏览、查找方面存在明显的局限性。提出了一个基于移动电话内容服务系统的个性化推荐系统．介绍了从寻找目标用户到实现推荐的全过程。实验结果表明。所介绍的个性化推荐系统可以有助于解决内容服务系统用户访问受限、资源迷茫的问题。     

An Adaptive Recommendation System without Explicit Acquisition of User Relevance Feedback

Recommendation systems are widely adopted in e-commerce businesses for helping customers locate products they would like to purchase. In an earlier work, we introduced a recommendation system, termed Yoda, which employs a hybrid approach that combines collaborative filtering (CF) and content-based querying to achieve higher accuracy for large-scale Web-based applications. To reduce the complexity of the hybrid approach, Yoda is structured as a tunable model that is trained off-line and employed for real-time recommendation on-line. The on-line process benefits from an optimized aggregation function with low complexity that allows the real-time aggregation based on confidence values of an active user to pre-defined sets of recommendations. In this paper, we extend Yoda to include more recommendation sets. The recommendation sets can be obtained from different sources, such as human experts, web navigation patterns, and clusters of user evaluations.More over, the extended Yoda can learn the confidence values automatically by utilizing implicit users' relevance feedback through web navigations using genetic algorithms (GA). Our end-to-end experiments show while Yoda's complexity is low and remains constant as the number of users and/or items grow, its accuracy surpasses that of the basic nearest-neighbor method by a wide margin (in most cases more than 100%). The experimental results also indicate that the retrieval accuracy is significantly increased by using the GA-based learning mechanism.     

A hybrid of sequential rules and collaborative filtering for product recommendation

Customers’ purchase behavior may vary over time. Traditional collaborative filtering (CF) methods make recommendations to a target customer based on the purchase behavior of customers whose preferences are similar to those of the target customer; however, the methods do not consider how the customers’ purchase behavior may vary over time. In contrast, the sequential rule-based recommendation method analyzes customers’ purchase behavior over time to extract sequential rules in the form: purchase behavior in previous periods ⇒ purchase behavior in the current period. If a target customer’s purchase behavior history is similar to the conditional part of the rule, then his/her purchase behavior in the current period is deemed to be the consequent part of the rule. Although the sequential rule method considers the sequence of customers’ purchase behavior over time, it does not utilize the target customer’s purchase data for the current period. To resolve the above problems, this work proposes a novel hybrid recommendation method that combines the segmentation-based sequential rule method with the segmentation-based KNN-CF method. The proposed method uses customers’ RFM (Recency, Frequency, and Monetary) values to cluster customers into groups with similar RFM values. For each group of customers, sequential rules are extracted from the purchase sequences of that group to make recommendations. Meanwhile, the segmentation-based KNN-CF method provides recommendations based on the target customer’s purchase data for the current period. Then, the results of the two methods are combined to make final recommendations. Experiment results show that the hybrid method outperforms traditional CF methods.     

新客户在商务站点中的个性化推荐

提出了一种针对新客户在商务站点购物的个性化推荐方法。首先利用已购物客户的浏览信息生成购物行为模型,得到新客户在站点中的浏览行为生成浏览行为模型,通过最近邻居的协同过滤技术生成与新客户行为最为相近的用户集,将最近邻居已购商品推荐给新客户。该方法能够给新客户提供及时准确的个性化商品信息。     

Exploiting two-faceted web of trust for enhanced-quality recommendations

Traditional collaborative filtering (CF) based recommender systems on the basis of user similarity often suffer from low accuracy because of the difficulty in finding similar users. Incorporating trust network into CF-based recommender system is an attractive approach to resolve the neighbor selection problem. Most existing trust-based CF methods assume that underlying relationships (whether inferred or pre-existing) can be described and reasoned in a web of trust. However, in online sharing communities or e-commerce sites, a web of trust is not always available and is typically sparse. The limited and sparse web of trust strongly affects the quality of recommendation. In this paper, we propose a novel method that establishes and exploits a two-faceted web of trust on the basis of users’ personal activities and relationship networks in online sharing communities or e-commerce sites, to provide enhanced-quality recommendations. The developed web of trust consists of interest similarity graphs and directed trust graphs and mitigates the sparsity of web of trust. Moreover, the proposed method captures the temporal nature of trust and interest by dynamically updating the two-faceted web of trust. Furthermore, this method adapts to the differences in user rating scales by using a modified Resnick’s prediction formula. As enabled by the Pareto principle and graph theory, new users highly benefit from the aggregated global interest similarity (popularity) in interest similarity graph and the global trust (reputation) in the directed trust graph. The experiments on two datasets with different sparsity levels (i.e., Jester and MovieLens datasets) show that the proposed approach can significantly improve the predictive accuracy and decision-support accuracy of the trust-based CF recommender system.     

基于客户喜好的双向个性化推荐算法

为了解决现有推荐算法仅考虑同类产品间单向推荐所缺乏的灵活性,提升产品的销量及用户的购物体验,提出一种基于客户喜好的双向个性化推荐算法,不仅可以为客户精准推荐产品,还可以为商家推荐潜在客户.首先,基于产品购买网络中客户及其邻居的购买信息,扩展客户购买信息;其次设计客户产品喜好权重计算办法,分析客户的购买喜好,并在客户喜好的指导下为客户提供个性化的产品推荐;最后,基于商家提供的样本客户,挖掘与样本客户相似的客户构成社区,为商家提供潜在客户推荐以及精准客户维护.在真实数据集上的实验验证了算法的有效性.该算法从客户和商家两个维度出发实现了产品与客户的双向推荐,为个性化推荐领域的研究提供有益的帮助.     

Ontology-based personalized couple clustering for heterogeneous product recommendation in mobile marketing

Plethora of cellular phones has been increasingly driving the spread of e-commerce mechanisms running on mobile devices. For instance, mobile marketing fulfills the wireless delivery (to the devices of mobile users) of the recommended product information and even one-to-one recommendations. One-to-one recommendation not only reduces the time that customers have to expend to for attaining appropriate products, but also is a method to engender customer values and develop the long-term customer relationships. This paper presents a one-to-one recommendation mechanism that iteratively takes as inputs the audio customer messages (together with product information) and produces personalized product analogy structures (that subsequently drive the generation of personalized heterogeneous product recommendations) based on the coupled clustering algorithm. The personalized product analogy structures also evolve as the messages (of the correspondent customer) grow. We have implemented the mechanism with J2EE Web Service that has produced fairly promising evaluation results.     

MONERS: A news recommender for the mobile web

Mobile web news services, which served by mobile service operators collecting news articles from diverse news contents providers, provide articles sorted by category or on the basis of attributes, such as the time at which they were posted. The mobile web should provide easy access to the categories or news contents preferred by users because user interface of wireless devices, particularly cell phones is limited for browsing between contents.This paper presents a mobile web news recommendation system (MONERS) that incorporates news article attributes and user preferences with regard to categories and news articles. User preference of news articles are estimated by aggregating news article importance and recency, user preference change, and user segment’s preference on news categories and articles. Performance of MONERS was tested in an actual mobile web environment; news organized by category had more page hits, while recommended news had a higher overall article read ratio.     

A WEB‐BASED PERSONALIZED BUSINESS PARTNER RECOMMENDATION SYSTEM USING FUZZY SEMANTIC TECHNIQUES

The web provides excellent opportunities to businesses in various aspects of development such as finding a business partner online. However, with the rapid growth of web information, business users struggle with information overload and increasingly find it difficult to locate the right information at the right time. Meanwhile, small and medium businesses (SMBs), in particular, are seeking “one‐to‐one” e‐services from government in current highly competitive markets. How can business users be provided with information and services specific to their needs, rather than an undifferentiated mass of information? An effective solution proposed in this study is the development of personalized e‐services. Recommender systems is an effective approach for the implementation of Personalized E‐Service which has gained wide exposure in e‐commerce in recent years. Accordingly, this paper first presents a hybrid fuzzy semantic recommendation (HFSR) approach which combines item‐based fuzzy semantic similarity and item‐based fuzzy collaborative filtering (CF) similarity techniques. This paper then presents the implementation of the proposed approach into an intelligent recommendation system prototype called Smart BizSeeker, which can recommend relevant business partners to individual business users, particularly for SMBs. Experimental results show that the HFSR approach can help overcome the semantic limitations of classical CF‐based recommendation approaches, namely sparsity and new “cold start” item problems.     

Personalized blog content recommender system for mobile phone users

Compared to newspaper columnists and broadcast media commentators, bloggers do not have organizations actively promoting their content to users; instead, they rely on word-of-mouth or casual visits by web surfers. We believe the WAP Push service feature of mobile phones can help bridge the gap between internet and mobile services, and expand the number of potential blog readers. Since mobile phone screen size is very limited, content providers must be familiar with individual user preferences in order to recommend content that matches narrowly defined personal interests. To help identify popular blog topics, we have created (a) an information retrieval process that clusters blogs into groups based on keyword analyses, and (b) a mobile content recommender system (M-CRS) for calculating user preferences for new blog documents. Here we describe results from a case study involving 20,000 mobile phone users in which we examined the effects of personalized content recommendations. Browsing habits and user histories were recorded and analyzed to determine individual preferences for making content recommendations via the WAP Push feature. The evaluation results of our recommender system indicate significant increases in both blog-related push service click rates and user time spent reading personalized web pages. The process used in this study supports accurate recommendations of personalized mobile content according to user interests. This approach can be applied to other embedded systems with device limitations, since document subject lines are elaborated and more attractive to intended users.     

Sequence-based trust in collaborative filtering for document recommendation

Collaborative filtering (CF) recommender systems have emerged in various applications to support item recommendation, which solve the information-overload problem by suggesting items of interest to users. Recently, trust-based recommender systems have incorporated the trustworthiness of users into CF techniques to improve the quality of recommendation. They propose trust computation models to derive the trust values based on users' past ratings on items. A user is more trustworthy if s/he has contributed more accurate predictions than other users. Nevertheless, conventional trust-based CF methods do not address the issue of deriving the trust values based on users' various information needs on items over time. In knowledge-intensive environments, users usually have various information needs in accessing required documents over time, which forms a sequence of documents ordered according to their access time. We propose a sequence-based trust model to derive the trust values based on users' sequences of ratings on documents. The model considers two factors – time factor and document similarity – in computing the trustworthiness of users. The proposed model enhanced with the similarity of user profiles is incorporated into a standard collaborative filtering method to discover trustworthy neighbors for making predictions. The experiment result shows that the proposed model can improve the prediction accuracy of CF method in comparison with other trust-based recommender systems.     

Typicality‐based collaborative filtering for book recommendation

Nowadays, personalized recommender system placed an important role to predict the customer needs, interest about particular product in various application domains, which is identified according to the product ratings. During this process, collaborative filtering (CF) has been utilized because it is one of familiar techniques in recommender systems. The conventional CF methods analyse historical interactions of user‐item pairs based on known ratings and then use these interactions to produce recommendations. The major challenge in CF is that it needs to calculate the similarity of each pair of users or items by observing the ratings of users on same item, whereas the typicality‐based CF determines the neighbours from user groups based on their typicality degree. Typicality‐based CF can predict the ratings of users with improved accuracy. However, to eliminate the cold start problem in the proposed recommender system, the demographic filtering method has been employed in addition to the typicality‐based CF. A weighted average scheme has been applied on the combined recommendation results of both typicality‐based CF and demographic‐based CF to produce the best recommendation result for the user. Thereby, the proposed system has been able to achieve a coverage ratio of more than 95%, which indicates that the system is able to provide better recommendation for the user from the available lot of products.     

User behaviours after critical mobile application incidents: the relationship with situational context

Users occasionally have critical incidents with information systems (IS). A critical IS incident is an IS product or service experience that a user considers to be unusually positive or negative. Critical IS incidents are highly influential in terms of users' overall perceptions and customer relationships; thus, they are crucial for IS product and service providers. Therefore, it is important to study user behaviours after such incidents. Within IS, the relationships between the situational context and user behaviours after critical incidents have not been addressed at all. Prior studies on general mobile use as a related research area have recognized the influence of the situational context, but they have not covered the relationships between specific situational characteristics and different types of user behaviours. To address this gap, we examine 605 critical mobile incidents that were collected from actual mobile application users. Based on our results, we extend current theoretical knowledge by uncovering and explaining the relationships between specific situational characteristics (interaction state, place, sociality and application type) and user behaviours (use continuance, word‐of‐mouth and complaints). We have found, for example, that users are less likely to engage in negative behaviours after negative incidents that take place outdoors or in vehicles than after indoor incidents. This is because users often consider indoor environments to be familiar and treat them with established expectations and low uncertainty: users are accustomed to the notion that the applications function indoors just like before. Further, we present practical implications for mobile application providers by suggesting to them which positive critical incidents are the most beneficial to promote and which negative critical incidents are the most crucial to avoid.     

基于移动用户上下文相似度的张量分解推荐算法

针对移动服务推荐中用户上下文环境复杂多变和数据稀疏性问题,提出一种基于移动用户上下文相似度的张量分解推荐算法——UCS-TF。该算法组合用户间的多维上下文相似度和上下文相似可信度,建立用户上下文相似度模型,再对目标用户的K个邻居用户建立移动用户-上下文-移动服务三维张量分解模型,获得目标用户的移动服务预测值,生成移动推荐。实验结果显示,与余弦相似性方法、Pearson相关系数方法和Cosine1改进相似度模型相比,所提UCS-TF算法表现最优时的平均绝对误差（MAE）分别减少了11.1%、10.1%和3.2%;其P@N指标大幅提升,均优于上述方法。另外,对比Cosine1算法、CARS2算法和TF算法,UCS-TF算法在数据稀疏密度为5%、20%、50%、80%上的预测误差最小。实验结果表明UCS-TF算法具有更好的推荐效果,同时将用户上下文相似度与张量分解模型结合,能有效缓解评分稀疏性的影响。     

基于社交信任和标签偏好的景点推荐方法

针对现有的景点推荐算法在处理用户关系时忽视了用户隐性信任和信任传递问题,以及当用户处于新城市时由于缺乏用户历史记录无法做出准确推荐的情况,本文提出一种综合用户信任关系和标签偏好的个性化景点推荐方法.在仅仅考虑用户相似度时推荐质量差的情况下引入信任度,通过挖掘用户隐性信任关系解决了现有研究在直接信任难以获取时无法做出推荐的情况,有效缓解了数据稀疏性和冷启动问题.同时在用户兴趣分析过程中将景点和标签的关系扩展到了用户、景点和标签三者的相互关系,把用户的兴趣偏好分解成对不同景点标签的长期偏好,有效地缓解了缺乏用户历史游览记录时推荐质量不佳的问题.通过在Flickr网站上收集的数据进行实验验证,结果表明本文提出的混合推荐算法有效地提高了推荐精度,在一定程度上缓解了冷启动和新城市问题.