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.     

Experimental evaluation of context-dependent collaborative filtering using item splitting

Collaborative Filtering (CF) computes recommendations by leveraging a historical data set of users’ ratings for items. CF assumes that the users’ recorded ratings can help in predicting their future ratings. This has been validated extensively, but in some domains the user’s ratings can be influenced by contextual conditions, such as the time, or the goal of the item consumption. This type of contextual information is not exploited by standard CF models. This paper introduces and analyzes a novel technique for context-aware CF called Item Splitting. In this approach items experienced in two alternative contextual conditions are “split” into two items. This means that the ratings of a split item, e.g., a place to visit, are assigned (split) to two new fictitious items representing for instance the place in summer and the same place in winter. This split is performed only if there is statistical evidence that under these two contextual conditions the items ratings are different; for instance, a place may be rated higher in summer than in winter. These two new fictitious items are then used, together with the unaffected items, in the rating prediction algorithm. When the system must predict the rating for that “split” item in a particular contextual condition (e.g., in summer), it will consider the new fictitious item representing the original one in that particular contextual condition, and will predict its rating. We evaluated this approach on real world, and semi-synthetic data sets using matrix factorization, and nearest neighbor CF algorithms. We show that Item Splitting can be beneficial and its performance depends on the method used to determine which items to split. We also show that the benefit of the method is determined by the relevance of the contextual factors that are used to split.     

Collaborative filtering and deep learning based recommendation system for cold start items

Recommender system is a specific type of intelligent systems, which exploits historical user ratings on items and/or auxiliary information to make recommendations on items to the users. It plays a critical role in a wide range of online shopping, e-commercial services and social networking applications. Collaborative filtering (CF) is the most popular approaches used for recommender systems, but it suffers from complete cold start (CCS) problem where no rating record are available and incomplete cold start (ICS) problem where only a small number of rating records are available for some new items or users in the system. In this paper, we propose two recommendation models to solve the CCS and ICS problems for new items, which are based on a framework of tightly coupled CF approach and deep learning neural network. A specific deep neural network SADE is used to extract the content features of the items. The state of the art CF model, timeSVD++, which models and utilizes temporal dynamics of user preferences and item features, is modified to take the content features into prediction of ratings for cold start items. Extensive experiments on a large Netflix rating dataset of movies are performed, which show that our proposed recommendation models largely outperform the baseline models for rating prediction of cold start items. The two proposed recommendation models are also evaluated and compared on ICS items, and a flexible scheme of model retraining and switching is proposed to deal with the transition of items from cold start to non-cold start status. The experiment results on Netflix movie recommendation show the tight coupling of CF approach and deep learning neural network is feasible and very effective for cold start item recommendation. The design is general and can be applied to many other recommender systems for online shopping and social networking applications. The solution of cold start item problem can largely improve user experience and trust of recommender systems, and effectively promote cold start items.     

Mathematical modeling of group product recommendation with partial information: How many ratings do we need?

Product recommendation is one of the most important services in the Internet. In this paper, we consider a product recommendation system which recommends products to a group of users. The recommendation system only has partial preference information on this group of users: a user only indicates his preference to a small subset of products in the form of ratings. This partial preference information makes it a challenge to produce an accurate recommendation. In this work, we explore a number of fundamental questions. What is the desired number of ratings per product so to guarantee an accurate recommendation? What are some effective voting rules in summarizing ratings? How users’ misbehavior such as cheating, in product rating may affect the recommendation accuracy? What are some efficient rating schemes? To answer these questions, we present a formal mathematical model of a group recommendation system. We formally analyze the model. Through this analysis we gain the insight to develop a randomized algorithm which is both computationally efficient and asymptotically accurate in evaluating the recommendation accuracy under a very general setting. We propose a novel and efficient heterogeneous rating scheme which requires equal or less rating workload, but can improve over a homogeneous rating scheme by as much as 30%. We carry out experiments on both synthetic data and real-world data from TripAdvisor. Not only we validate our model, but also we obtain a number of interesting observations, i.e., a small of misbehaving users can decrease the recommendation accuracy remarkably. For TripAdvisor, one hundred ratings per product is sufficient to guarantee a high accuracy recommendation. We believe our model and methodology are important building blocks to refine and improve applications of group recommendation systems.     

A hybrid online-product recommendation system: Combining implicit rating-based collaborative filtering and sequential pattern analysis

Many online shopping malls in which explicit rating information is not available still have difficulty in providing recommendation services using collaborative filtering (CF) techniques for their users. Applying temporal purchase patterns derived from sequential pattern analysis (SPA) for recommendation services also often makes users unhappy with the inaccurate and biased results obtained by not considering individual preferences. The objective of this research is twofold. One is to derive implicit ratings so that CF can be applied to online transaction data even when no explicit rating information is available, and the other is to integrate CF and SPA for improving recommendation quality. Based on the results of several experiments that we conducted to compare the performance between ours and others, we contend that implicit rating can successfully replace explicit rating in CF and that the hybrid approach of CF and SPA is better than the individual ones.     

Distributed collaborative filtering with singular ratings for large scale recommendation

Collaborative filtering (CF) is an effective technique addressing the information overloading problem, where each user is associated with a set of rating scores on a set of items. For a chosen target user, conventional CF algorithms measure similarity between this user and other users by utilizing pairs of rating scores on common rated items, but discarding scores rated by one of them only. We call these comparative scores as dual ratings, while the non-comparative scores as singular ratings. Our experiments show that only about 10% ratings are dual ones that can be used for similarity evaluation, while the other 90% are singular ones. In this paper, we propose SingCF approach, which attempts to incorporate multiple singular ratings, in addition to dual ratings, to implement collaborative filtering, aiming at improving the recommendation accuracy. We first estimate the unrated scores for singular ratings and transform them into dual ones. Then we perform a CF process to discover neighborhood users and make predictions for each target user. Furthermore, we provide a MapReduce-based distributed framework on Hadoop for significant improvement in efficiency. Experiments in comparison with the state-of-the-art methods demonstrate the performance gains of our approaches.     

A novel collaborative filtering approach for recommending ranked items

In recent years, Collaborative Filtering (CF) has proven to be one of the most successful techniques used in recommendation systems. Since current CF systems estimate the ratings of not-yet-rated items based on other items’ ratings, these CF systems fail to recommend products when users’ preferences are not expressed in numbers. In many practical situations, however, users’ preferences are represented by ranked lists rather than numbers, such as lists of movies ranked according to users’ preferences. Therefore, this study proposes a novel collaborative filtering methodology for product recommendation when the preference of each user is expressed by multiple ranked lists of items. Accordingly, a four-staged methodology is developed to predict the rankings of not-yet-ranked items for the active user. Finally, a series of experiments is performed, and the results indicate that the proposed methodology produces high-quality recommendations.     

融合Jensen-Shannon散度的推荐算法

为 充分利用所有评分,缓解数据稀疏性问题,将概率统计领域的Jensen-Shannon(JS)散度引入相似性度量中,提出了一种新的项目相似性度量算法。该 算法将项目的评分信息转化为评分值密度,并依据评分值的密度分布来计算项目相似性。同时,引入评分数量因子,进一步提升了基于JS的相似性度量方法的性能。最后,以基于JS的相似性度量方法为基础,设计了相应的协同过滤算法。在MovieLens数据集上的实验结果表明,所提算法在预测误差和推荐准确性方面均有良好的表现。因此,该算法在推荐系统中具有很好的应用潜力。     

Improvement of collaborative filtering using rating normalization

With the advent of the Internet, the types and amount of information one can access have increased dramatically. In today’s overwhelming information environment, recommendation systems that quickly analyze large amounts of available information and help users find items of interest are increasingly needed. This paper proposes an improvement of an existing preference prediction algorithm to increase the accuracy of recommendation systems. In a recommendation system, prediction of items preferred by users is based on their ratings. However, individual users with the same degree of satisfaction to an item may give different ratings to the item. We intend to make more precise preference prediction by perceiving differences in users’ rating dispositions. The proposed method consists of two processes of perceiving users’ rating dispositions with clustering and of performing rating normalization according to such rating dispositions. The experimental results show that our method yields higher performance than ordinary collaborative filtering approach.     

融合用户兴趣和评分差异的协同过滤推荐算法

针对传统的协同过滤算法中单一评分相似性计算不准确的问题,提出融合用户兴趣和评分差异的协同过滤推荐算法。将TF-IDF思想运用到用户对标签的权重计算中,并使用指数衰减函数和时间窗口捕捉用户兴趣的变化;根据历史评分矩阵,充分考虑用户评分值差异、评判准则差异、影响力差异和项目影响差异等影响因子,定义了一种评分差异相似性度量算法;最后将用户兴趣相似性和评分差异相似性进行加权融合,获取更加准确的用户邻居,从而预测项目评分并进行推荐。在数据集Movielens的实验表明,提出的算法能有效提高推荐精度。     

Opinion-enhanced collaborative filtering for recommender systems through sentiment analysis

The motivation of collaborative filtering (CF) comes from the idea that people often get the best recommendations from someone with similar tastes. With the growing popularity of opinion-rich resources such as online reviews, new opportunities arise as we can identify the preferences from user opinions. The main idea of our approach is to elicit user opinions from online reviews, and map such opinions into preferences that can be understood by CF-based recommender systems. We divide recommender systems into two types depending on the number of product category recommended: the multiple-category recommendation and the single-category recommendation. For the former, sentiment polarity in coarse-grained manner is identified while for the latter fine-grained sentiment analysis is conducted for each product aspect. If the evaluation frequency for an aspect by a user is greater than the average frequency by all users, it indicates that the user is more concerned with that aspect. If a user's rating for an aspect is lower than the average rating by all users, he or she is much pickier than others on that aspect. Through sentiment analysis, we then build an opinion-enhanced user preference model, where the higher the similarity between user opinions the more consistent preferences between users are. Experiment results show that the proposed CF algorithm outperforms baseline methods for product recommendation in terms of accuracy and recall.     

Composite recommendations: from items to packages

Classical recommender systems provide users with a list of recommendations where each recommendation consists of a single item, e.g., a book or DVD. However, several applications can benefit from a system capable of recommending packages of items, in the form of sets. Sample applications include travel planning with a limited budget (price or time) and twitter users wanting to select worthwhile tweeters to follow, given that they can deal with only a bounded number of tweets. In these contexts, there is a need for a system that can recommend the top-k packages for the user to choose from. Motivated by these applications, we consider composite recommendations, where each recommendation comprises a set of items. Each item has both a value (rating) and a cost associated with it, and the user specifies a maximum total cost (budget) for any recommended set of items. Our composite recommender system has access to one or more component recommender systems focusing on different domains, as well as to information sources which can provide the cost associated with each item. Because the problem of decidingwhether there is a recommendation (package)whose cost is under a given budget and whose value exceeds some threshold is NP-complete, we devise several approximation algorithms for generating the top-k packages as recommendations. We analyze the efficiency as well as approximation quality of these algorithms. Finally, using two real and two synthetic datasets, we subject our algorithms to thorough experimentation and empirical analysis. Our findings attest to the efficiency and quality of our approximation algorithms for the top-k packages compared to exact algorithms.     

Coherence and inconsistencies in rating behavior: estimating the magic barrier of recommender systems

Recommender Systems have to deal with a wide variety of users and user types that express their preferences in different ways. This difference in user behavior can have a profound impact on the performance of the recommender system. Users receive better (or worse) recommendations depending on the quantity and the quality of the information the system knows about them. Specifically, the inconsistencies in users’ preferences impose a lower bound on the error the system may achieve when predicting ratings for one particular user—this is referred to as the magic barrier. In this work, we present a mathematical characterization of the magic barrier based on the assumption that user ratings are afflicted with inconsistencies—noise. Furthermore, we propose a measure of the consistency of user ratings (rating coherence) that predicts the performance of recommendation methods. More specifically, we show that user coherence is correlated with the magic barrier; we exploit this correlation to discriminate between easy users (those with a lower magic barrier) and difficult ones (those with a higher magic barrier). We report experiments where the recommendation error for the more coherent users is lower than that of the less coherent ones. We further validate these results by using two public datasets, where the necessary data to identify the magic barrier is not available, in which we obtain similar performance improvements.     

Hybrid recommendations for mobile commerce based on mobile phone features

Mobile data communications have evolved as the number of third generation (3G) subscribers has increased. The evolution has triggered an increase in the use of mobile devices, such as mobile phones, to conduct mobile commerce and mobile shopping on the mobile web. There are fewer products to browse on the mobile web; hence, one‐to‐one marketing with product recommendations is important. Typical collaborative filtering (CF) recommendation systems make recommendations to potential customers based on the purchase behaviour of customers with similar preferences. However, this method may suffer from the so‐called sparsity problem, which means there may not be sufficient similar users because the user‐item rating matrix is sparse. In mobile shopping environments, the features of users' mobile phones provide different functionalities for using mobile services; thus, the features may be used to identify users with similar purchase behaviour. In this paper, we propose a mobile phone feature (MPF)‐based hybrid method to resolve the sparsity issue of the typical CF method in mobile environments. We use the features of mobile phones to identify users' characteristics and then cluster users into groups with similar interests. The hybrid method combines the MPF‐based method and a preference‐based method that uses association rule mining to extract recommendation rules from user groups and make recommendations. Our experiment results show that the proposed hybrid method performs better than other recommendation methods.     

Collaborative filtering with ordinal scale-based implicit ratings for mobile music recommendations

Collaborative filtering (CF)-based recommender systems represent a promising solution for the rapidly growing mobile music market. However, in the mobile Web environment, a traditional CF system that uses explicit ratings to collect user preferences has a limitation: mobile customers find it difficult to rate their tastes directly because of poor interfaces and high telecommunication costs. Implicit ratings are more desirable for the mobile Web, but commonly used cardinal (interval, ratio) scales for representing preferences are also unsatisfactory because they may increase estimation errors. In this paper, we propose a CF-based recommendation methodology based on both implicit ratings and less ambitious ordinal scales. A mobile Web usage mining (mWUM) technique is suggested as an implicit rating approach, and a specific consensus model typically used in multi-criteria decision-making (MCDM) is employed to generate an ordinal scale-based customer profile. An experiment with the participation of real mobile Web customers shows that the proposed methodology provides better performance than existing CF algorithms in the mobile Web environment.     

基于社交信任聚类的混合推荐算法

推荐系统能够有效地解决信息过载问题,其中,协同过滤（collaborative filtering,简称CF）是推荐系统广泛采用的技术之一.然而传统的CF技术存在可扩展性差、数据稀疏和推荐结果精度低等问题.为了提高推荐质量,将信任关系融合到推荐系统中,采用聚类（FCM）方法,对信任关系进行聚类.利用信任类预测用户间的隐式信任,最后将信任关系与用户-项目关系线性融合进行推荐.在Douban和Epinions数据集上的实验结果表明,与传统的基于CF、基于信任和用户项目聚类的推荐算法相比,该算法能够大幅度地改进推荐质量,提升算法的时间效率.     

基于Tag和协同过滤的混合推荐方法

针对传统协同过滤方法的稀疏性问题,提出基于标签(Tag)和协同过滤的混合推荐方法TAG-CF。通过Tag分类信息获取项目的最近邻居,采用基于项目的最近邻方法预测用户评分值,并利用该预测值填充用户评分矩阵,构造密集的伪矩阵,运用基于用户的的协同过滤方法在伪矩阵上计算用户对项目的预测评分值。实验结果表明,TAG-CF能有效降低推荐系统的平均绝对误差,提高推荐质量。     

Mining changes in customer buying behavior for collaborative recommendations

The preferences of customers change over time. However, existing collaborative filtering (CF) systems are static, since they only incorporate information regarding whether a customer buys a product during a certain period and do not make use of the purchase sequences of customers. Therefore, the quality of the recommendations of the typical CF could be improved through the use of information on such sequences.In this study, we propose a new methodology for enhancing the quality of CF recommendation that uses customer purchase sequences. The proposed methodology is applied to a large department store in Korea and compared to existing CF techniques. Various experiments using real-world data demonstrate that the proposed methodology provides higher quality recommendations than do typical CF techniques, with better performance, especially with regard to heavy users.     

Predicting airline customers’ recommendations using qualitative and quantitative contents of online reviews

Customers generally give ratings and reviews for different services that they get online or offline. These reviews and ratings aspects are effectively helpful to both the company and customers to receive feedback and make the right decisions, respectively. However, the number of reviews and ratings can increase exponentially, bringing a new challenge for the company to manage and track. Under these circumstances, it will also be hard for the customer to make the right decision. In this work, we summarize text reviews and ratings given by passengers for different airlines. The objective of this research is to predict whether the recommendation made by the customer is positive or negative. Two types of features, namely, textual feature and explicit ratings, are extracted from the dataset and other attributes. We found the relationship between such sentiments and feelings expressed in online reviews and predictive consumer recommendation decisions. We have considered quantitative content with qualitative content of online reviews in predicting recommendation decisions, which shows the work’s novelty. Additionally, the obtained results yield an essential contribution to the existing literature in terms of service evaluation, making managerial policies, and predictive consumer recommendations, etc. Moreover, we hope that this work would be helpful for practitioners who wish to utilize the technique to make the quick and essential hidden information by combining textual reviews and various service aspects ratings.

     

Collaborative filtering with interlaced generalized linear models

Collaborative filtering (CF) is a data analysis task appearing in many challenging applications, in particular data mining in Internet and e-commerce. CF can often be formulated as identifying patterns in a large and mostly empty rating matrix. In this paper, we focus on predicting unobserved ratings. This task is often a part of a recommendation procedure. We propose a new CF approach called interlaced generalized linear models (GLM); it is based on a factorization of the rating matrix and uses probabilistic modeling to represent uncertainty in the ratings. The advantage of this approach is that different configurations, encoding different intuitions about the rating process can easily be tested while keeping the same learning procedure. The GLM formulation is the keystone to derive an efficient learning procedure, applicable to large datasets. We illustrate the technique on three public domain datasets.