一种改进的基于协同过滤的个性化推荐算法

随着网络信息量的日益增加,为用户提供个性化服务是一种趋势。协同过滤又是个性化推荐中应用最成功的技术,我们针对个性化推荐算法做出了改进,仿真实验表明我们的方法比传统的方法有着更高的推荐质量。

协同过滤推荐算法的优化

随着网络技术的发展,用户向互联网输送的信息和数据的量在极数的增加,人们难以从茫茫的信息大海中获取有价值的信息或数据,本文对目前使用的协同过滤推荐算法进行了改进,提出了基于 MALS 矩阵分解的协同过滤算法。

基于P2P网络的信息过滤与推荐技术研究

共享信息的集中存储对存放这些信息的服务器提出了较高的要求,同时,服务器将成为整个系统的瓶颈。为此,提出了一种基于P2P的信息共享与推荐模型,解决了信息集中存放产生的问题。接着,对该模型中涉及到的基于内容的过滤,提出了一种基于词汇链的方法,较好地解决了纯粹单一关键词无法准确描述文本的问题,并对信息推荐中使用最成功的协同过滤算法进行了描述。给出了文本过滤的实验结果及其分析。

A novel similarity measurement approach considering intrinsic user groups in collaborative filtering

To improve the similarity measurement between users, a similarity measurement approach incorporating clusters of intrinsic user groups（ SMCUG） is proposed considering the social information of users. The approach constructs the taxonomy trees for each categorical attribute of users. Based on the taxonomy trees, the distance between numerical and categorical attributes is computed in a unified framework via a proper weight. Then, using the proposed distance method, the nave k-means cluster method is modified to compute the intrinsic user groups. Finally, the user group information is incorporated to improve the performance of traditional similarity measurement. A series of experiments are performed on a real world dataset, M ovie Lens. Results demonstrate that the proposed approach considerably outperforms the traditional approaches in the prediction accuracy in collaborative filtering.

A Probabilistic Rating Prediction and Explanation Inference Model for Recommender Systems

Collaborative Filtering(CF) is a leading approach to build recommender systems which has gained considerable development and popularity. A predominant approach to CF is rating prediction recommender algorithm, aiming to predict a user's rating for those items which were not rated yet by the user. However, with the increasing number of items and users, thedata is sparse.It is difficult to detectlatent closely relation among the items or users for predicting the user behaviors. In this paper,we enhance the rating prediction approach leading to substantial improvement of prediction accuracy by categorizing according to the genres of movies. Then the probabilities that users are interested in the genres are computed to integrate the prediction of each genre cluster. A novel probabilistic approach based on the sentiment analysis of the user reviews is also proposed to give intuitional explanations of why an item is recommended.To test the novel recommendation approach, a new corpus of user reviews on movies obtained from the Internet Movies Database(IMDB) has been generated. Experimental results show that the proposed framework is effective and achieves a better prediction performance.

Time-Ordered Collaborative Filtering for News Recommendation

Faced with hundreds of thousands of news articles in the news websites,it is difficult for users to find the news articles they are interested in.Therefore,various news recommender systems were built.In the news recommendation,these news articles read by a user is typically in the form of a time sequence.However,traditional news recommendation algorithms rarely consider the time sequence characteristic of user browsing behaviors.Therefore,the performance of traditional news recommendation algorithms is not good enough in predicting the next news article which a user will read.To solve this problem,this paper proposes a time-ordered collaborative filtering recommendation algorithm(TOCF),which takes the time sequence characteristic of user behaviors into account.Besides,a new method to compute the similarity among different users,named time-dependent similarity,is proposed.To demonstrate the efficiency of our solution,extensive experiments are conducted along with detailed performance analysis.

Multi-Domain Collaborative Recommendation with Feature Selection

Collaborative f iltering, as one of the most popular techniques, plays an important role in recommendation systems. However,when the user-item rating matrix is sparse,its performance will be degenerate. Recently,domain-specific recommendation approaches have been developed to address this problem.The basic idea is to partition the users and items into overlapping domains, and then perform recommendation in each domain independently. Here, a domain means a group of users having similar preference to a group of products. However, these domain-specific methods consisting of two sequential steps ignore the mutual benefi t of domain segmentation and recommendation. Hence, a unified framework is presented to simultaneously realize recommendation and make use of the domain information underlying the rating matrix in this paper. Based on matrix factorization,the proposed model learns both user preferences of multiple domains and preference selection vectors to select relevant features for each group of products. Besides, local context information is utilized from the user-item rating matrix to enhance the new framework.Experimental results on two widely used datasets, e.g., Ciao and Epinions, demonstrate the effectiveness of our proposed model.

A SON solution for cell outage detection using a cooperative prediction approach

In order to improve the efficiency of automatic management and self-healing of the self-organizing network(SON),a cell outage problem is investigated and a cooperative prediction-based automatic cell outage detection algorithm is proposed.By the improved collaborative filtering prediction algorithm,the location correlation of users in the wireless network is considered.By incorporating the cooperative grey model prediction algorithm,the time correlation of users motion trajectory is also introduced.Data of users in a normal scenario is simulated and collected for model training and threshold calculating and the outage cell can be effectively detected using the proposed approach.The simulation results demonstrate that the proposed scheme has a higher detection rate for different extents of outage while ensuring the lower communication overhead and false alarm rate than traditional outage detection methods.The detection rate of the proposed approach outperforms the traditional method by around 14%,especially when there are sparse users in the network,and it is able to detect the outage cell with no active users with the help of neighbor cells.

Performance evaluation on field synergy and composite regeneration by coupling cerium-based additive and microwave for a diesel particulate filter

In order to reveal the mechanics of composite regeneration by coupling cerium-based additive and microwave for a diesel particulate filter, a composite regeneration model by coupling cerium-based additive and microwave for a diesel particulate filter was established based on field synergy theory. Performance evaluation on field synergy and composite regeneration of the diesel particulate filter was conducted by using the vortex crushing combustion and field synergy mathematical models. The results show that the peak temperature of the particulate filter body reaches 1180-1190 K when the regeneration time is 175 s, and there are optimal coordination degree between the velocity vector and temperature gradient of the filter body and the maximum ratio0.56-0.60 of the best burning regeneration region is obtained. Accordingly, the largest regeneration combustion rate inside the particulate filter body and the highest regeneration efficiency at the moment are achieved.

A FastSLAM Algorithm Based on the Improved Auxiliary Particle Filter with Stirling Interpolation

The choice of the particle's distribution model and the consistency of the result are very important for FastSLAM.The improved auxiliary variable model with FastSLAM,and Stirling Interpolation which is used to approximate the nonlinear functions are provided.This approach improves the precision of the approximation for the nonlinear functions,conquers the drawback of the FastSLAM1.0 by using a model ignoring the measurement data,enhances the estimation consistency of the robot pose,and reduces the degradation speed of the particle in FastSLAM algorithm.Simulation results demonstrate the excellence of the proposed algorithm and give the noise parameter influence on the proposed algorithm.

A microblog recommendation algorithm based on social tagging and a temporal interest evolution model

Personalized microblog recommendations face challenges of user cold-start problems and the interest evolution of topics. In this paper, we propose a collaborative filtering recommendation algorithm based on a temporal interest evolution model and social tag prediction. Three matrices are first prepared to model the relationship between users, tags, and microblogs. Then the scores of the tags for each microblog are optimized according to the interest evolution model of tags. In addition, to address the user cold-start problem, a social tag prediction algorithm based on community discovery and maximum tag voting is designed to extract candidate tags for users. Finally, the joint probability of a tag for each user is calculated by integrating the Bayes probability on the set of candidate tags, and the top n microblogs with the highest joint probabilities are recommended to the user. Experiments using datasets from the microblog of Sina Weibo showed that our algorithm achieved good recall and precision in terms of both overall and temporal performances. A questionnaire survey proved user satisfaction with recommendation results when the cold-start problem occurred.

A novel confidence estimation method for heterogeneous implicit feedback

Implicit feedback, which indirectly reflects opinion through user behaviors, has gained increasing attention in recommender system communities due to its accessibility and richness in real-world applications. A major way of exploiting implicit feedback is to treat the data as an indication of positive and negative preferences associated with vastly varying confidence levels. Such algorithms assume that the numerical value of implicit feedback, such as time of watching, indicates confidence, rather than degree of preference, and a larger value indicates a higher confidence, although this works only when just one type of implicit feedback is available. However, in real-world applications, there are usually various types of implicit feedback, which can be referred to as heterogeneous implicit feedback. Existing methods cannot efficiently infer confidence levels from heterogeneous implicit feedback. In this paper, we propose a novel confidence estimation approach to infer the confidence level of user preference based on heterogeneous implicit feedback. Then we apply the inferred confidence to both point-wise and pair-wise matrix factorization models, and propose a more generic strategy to select effective training samples for pair-wise methods. Experiments on real-world e-commerce datasets from Tmall.com show that our methods outperform the state-of-the-art approaches, consid- ering several commonly used ranking-oriented evaluation criteria.

Preference transfer model in collaborative filtering for implicit data

Generally, predicting whether an item will be liked or disliked by active users, and how much an item will be liked, is a main task of collaborative filtering systems or recommender systems. Recently, predicting most likely bought items for a target user, which is a subproblem of the rank problem of collaborative filtering, became an important task in collaborative filtering. Traditionally, the prediction uses the user item co-occurrence data based on users' buying behaviors. However, it is challenging to achieve good prediction performance using traditional methods based on single domain information due to the extreme sparsity of the buying matrix. In this paper, we propose a novel method called the preference transfer model for effective cross-domain collaborative filtering. Based on the preference transfer model, a common basis item-factor matrix and different user-factor matrices are factorized.Each user-factor matrix can be viewed as user preference in terms of browsing behavior or buying behavior. Then,two factor-user matrices can be used to construct a so-called ‘preference dictionary' that can discover in advance the consistent preference of users, from their browsing behaviors to their buying behaviors. Experimental results demonstrate that the proposed preference transfer model outperforms the other methods on the Alibaba Tmall data set provided by the Alibaba Group.