2,911 research outputs found
Application of Improved Collaborative Filtering in the Recommendation of E-commerce Commodities
Problems such as low recommendation precision and efficiency often exist in traditional collaborative filtering because of the huge basic data volume. In order to solve these problems, we proposed a new algorithm which combines collaborative filtering and support vector machine (SVM). Different with traditional collaborative filtering, we used SVM to classify commodities into positive and negative feedbacks. Then we selected the commodities that have positive feedback to calculate the comprehensive grades of marks and comments. After that, we build SVM-based collaborative filtering algorithm. Experiments on Taobao data (a Chinese online shopping website owned by Alibaba) showed that the algorithm has good recommendation precision and recommendation efficiency, thus having certain practical value in the E-commerce industry
Constraint-based Recommender System for Commodity Realization
In this paper, we suggest a novel recommender system where a set of appropriate propositions is formed by measuring how user query features are close to space of all possible propositions. The system is for e-traders selling commodities. A commodity has hierarchical-structure properties which are mapped to the respective numerical scales. The scales are normalized so that a query from a potential customer and any possible proposition from the e-trader is a multidimensional point of a nonnegative unit hypercube put on the coordinate origin. The user can weight levels. The distance between the query and propositions are measured by the respective metric in the Euclidean arithmetic space. The best proposition is defined by the shortest distance. Top N propositions are defined by N shortest distances. The system does not depend on any user experience, nor on the e-trader tendency to impose one’s preferences on the customer
Personalized Recommendation Model: An Online Comment Sentiment Based Analysis
Traditional recommendation algorithms measure users’ online ratings of goods and services but ignore the information contained in written reviews, resulting in lowered personalized recommendation accuracy. Users’ reviews express opinions and reflect implicit preferences and emotions towards the features of products or services. This paper proposes a model for the fine-grained analysis of emotions expressed in users’ online written reviews, using film reviews on the Chinese social networking site Douban.com as an example. The model extracts feature-sentiment word pairs in user reviews according to four syntactic dependencies, examines film features, and scores the sentiment values of film features according to user preferences. User group personalized recommendations are realized through user clustering and user similarity calculation. Experiments show that the extraction of user feature-sentiment word pairs based on four syntactic dependencies can better identify the implicit preferences of users, apply them to recommendations and thereby increase recommendation accuracy
A Survey of Matrix Completion Methods for Recommendation Systems
In recent years, the recommendation systems have become increasingly popular and have been used in a broad variety of applications. Here, we investigate the matrix completion techniques for the recommendation systems that are based on collaborative filtering. The collaborative filtering problem can be viewed as predicting the favorability of a user with respect to new items of commodities. When a rating matrix is constructed with users as rows, items as columns, and entries as ratings, the collaborative filtering problem can then be modeled as a matrix completion problem by filling out the unknown elements in the rating matrix. This article presents a comprehensive survey of the matrix completion methods used in recommendation systems. We focus on the mathematical models for matrix completion and the corresponding computational algorithms as well as their characteristics and potential issues. Several applications other than the traditional user-item association prediction are also discussed
Learning from History and Present: Next-item Recommendation via Discriminatively Exploiting User Behaviors
In the modern e-commerce, the behaviors of customers contain rich
information, e.g., consumption habits, the dynamics of preferences. Recently,
session-based recommendations are becoming popular to explore the temporal
characteristics of customers' interactive behaviors. However, existing works
mainly exploit the short-term behaviors without fully taking the customers'
long-term stable preferences and evolutions into account. In this paper, we
propose a novel Behavior-Intensive Neural Network (BINN) for next-item
recommendation by incorporating both users' historical stable preferences and
present consumption motivations. Specifically, BINN contains two main
components, i.e., Neural Item Embedding, and Discriminative Behaviors Learning.
Firstly, a novel item embedding method based on user interactions is developed
for obtaining an unified representation for each item. Then, with the embedded
items and the interactive behaviors over item sequences, BINN discriminatively
learns the historical preferences and present motivations of the target users.
Thus, BINN could better perform recommendations of the next items for the
target users. Finally, for evaluating the performances of BINN, we conduct
extensive experiments on two real-world datasets, i.e., Tianchi and JD. The
experimental results clearly demonstrate the effectiveness of BINN compared
with several state-of-the-art methods.Comment: 10 pages, 7 figures, KDD 201
An Efficient Recommendation System in E-commerce using Passer learning optimization based on Bi-LSTM
Recommendation system services have become crucial for users to access
personalized goods or services as the global e-commerce market expands. They
can increase business sales growth and lower the cost of user information
exploration. Recent years have seen a signifi-cant increase in researchers
actively using user reviews to solve standard recommender system research
issues. Reviews may, however, contain information that does not help consumers
de-cide what to buy, such as advertising or fictitious or fake reviews. Using
such reviews to offer suggestion services may reduce the effectiveness of those
recommendations. In this research, the recommendation in e-commerce is
developed using passer learning optimization based on Bi-LSTM to solve that
issue (PL optimized Bi-LSTM). Data is first obtained from the product
recommendation dataset and pre-processed to remove any values that are missing
or incon-sistent. Then, feature extraction is performed using TF-IDF features
and features that support graph embedding. Before submitting numerous features
with the same dimensions to the Bi-LSTM classifier for analysis, they are
integrated using the feature concatenation approach. The Collaborative Bi-LSTM
method employs these features to determine if the model is a recommended
product. The PL optimization approach, which efficiently adjusts the
classifier's parameters and produces an extract output that measures the
f1-score, MSE, precision, and recall, is the basis of this research's
contributions. As compared to earlier methods, the pro-posed PL-optimized
Bi-LSTM achieved values of 88.58%, 1.24%, 92.69%, and 92.69% for dataset 1,
88.46%, 0.48%, 92.43%, and 93.47% for dataset 2, and 92.51%, 1.58%, 91.90%, and
90.76% for dataset 3
A Dynamic Trust Relations-Based Friend Recommendation Algorithm in Social Network Systems
A discovered algorithm based on the dynamic trust relations of users in a social network system (SNS) was proposed aiming at getting useful information more efficiently in an SNS. The proposed dynamic model combined the interests and trust relations of users to explore their good friends for recommendations. First, the network based on the interests and trust relations of users was set up. Second, the temporal factor was added to the model, then a dynamic model of the degree of the interest and trust relations of the users was calculated. Lastly, the similarities among the users were measured via this dynamic model, and the recommendation list of good friends was achieved. Results showed that the proposed algorithm effectively described the changes in the interest similarities and trust relations of users with time, and the recommended result was more accurate and effective than the traditional ones
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