An interpretable mechanism for personalized recommendation based on cross feature

2021 ◽  
pp. 1-12
Author(s):  
Lv YE ◽  
Yue Yang ◽  
Jian-Xu Zeng
Keyword(s):  
Recommender System ◽  
Online Shopping ◽  
Side Information ◽  
Attention Mechanism ◽  
Personalized Recommendation ◽  
Model Based ◽  
Embedded Model ◽  
Feature Attention ◽  
Match Score

The existing recommender system provides personalized recommendation service for users in online shopping, entertainment, and other activities. In order to improve the probability of users accepting the system’s recommendation service, compared with the traditional recommender system, the interpretable recommender system will give the recommendation reasons and results at the same time. In this paper, an interpretable recommendation model based on XGBoost tree is proposed to obtain comprehensible and effective cross features from side information. The results are input into the embedded model based on attention mechanism to capture the invisible interaction among user IDs, item IDs and cross features. The captured interactions are used to predict the match score between the user and the recommended item. Cross-feature attention score is used to generate different recommendation reasons for different user-items.Experimental results show that the proposed algorithm can guarantee the quality of recommendation. The transparency and readability of the recommendation process has been improved by providing reference reasons. This method can help users better understand the recommendation behavior of the system and has certain enlightenment to help the recommender system become more personalized and intelligent.

A Personalized Recommendation Model Based on Contextual Information

2015 ◽  
Vol 713-715 ◽  
pp. 1530-1533
Author(s):  
Yuan Zi He
Keyword(s):  
Recommender System ◽  
Information Overload ◽  
Contextual Information ◽  
User Model ◽  
Personalized Recommendation ◽  
Contextual Data ◽  
Model Based ◽  
Structure Text ◽  
Novel Model

Personalized recommendation offers a new way to solve the problem of information overload. In order to effectively build user model and improve the effect of personalized recommendation, this paper proposes a novel model for mining contextual information of non-structure text, and insects the contextual information into user model, which enriches user model. The experiment results shown that the model can greatly improve the recommendation performance when the model is applied to contextual data of the recommender system in hotel.

PKM Kelompok Tani Tan Selo 1 dan Tan Selo 2 Desa Sukomaju Kecamatan Srono Kabupaten Banyuwangi Jawa Timur dalam Meningkatkan Nilai Ekonomis Pisang dan Strategi Pemasarannya

2018 ◽  
Vol 2 (2) ◽  
pp. 73
Author(s):  
Fitri Nurmasari ◽  
Raup Padillah
Keyword(s):  
Community Service ◽  
Online Shopping ◽  
Agricultural Land ◽  
Economic Value ◽  
Marketing Strategies ◽  
Service Programs ◽  
Community Service Programs ◽  
Farmer Groups ◽  
The Village

Banyuwangi Regency is one of the agricultural centers in East Java province and Indonesia. Mostly,Banyuwangi people work as farmers due to the fertil soil and wide amount of agricultural land in Banyuwangi . Thelarge number of people who work as farmers initiating the formation of farmer groups. One of the farmer groups in theSrono sub-district of Banyuwangi is the "Tan Selo 1" farmers group located in the village of Sukomaju and the "TanSelo 2" farmers group in Sukonatar village. The normal average price of one banana bunch in Banyuwangi is between50-60 thousand depending on the type and quality of bananas. Problems arise when the quantity of bananas in the marketarose, the price of 1 bunch of bananas decreases dramatically. The price of 1 bunch which is usually set at 50-60thousand drops drastically to only 20-30 thousand. This is certainly a problem for farmers in the Tan Selo group. The lackof knowledge of Tan Selo farmers about alternative variants of processed banana based products and the lack ofknowledge of the marketing strategies make it hard for the Tan Selo farmers to increase the economic value of bananaswhich have been used as an alternative income for farmers. Therefore, the solutions offered to overcome the problems offarmers include: equipping and improving farmers' knowledge about the variety of processed banana-based foods andtheir marketing strategies, conducting training to make variations on banana-based foods, conducting training oneffective marketing strategies. Overall, a series of community service programs were carried out perfectly as it expected.The percentage of participants' understanding in choosing high quality bananas is 85%, the percentage of participants’ability in processing banana-based foods is 86%, and percentage of participants who successfully sell processed foodproducts by utilizing online shopping sites is 70%

scholarly journals Research of Personalized Recommendation Technology Based on Knowledge Graphs

2021 ◽  
Vol 11 (15) ◽  
pp. 7104
Author(s):  
Xu Yang ◽  
Ziyi Huan ◽  
Yisong Zhai ◽  
Ting Lin
Keyword(s):  
Neural Network ◽  
Hot Spot ◽  
Experimental Results ◽  
Graph Representation ◽  
Personalized Recommendation ◽  
Knowledge Graph ◽  
Learning Technology ◽  
Data Set ◽  
Model Based ◽  
Knowledge Graphs

Nowadays, personalized recommendation based on knowledge graphs has become a hot spot for researchers due to its good recommendation effect. In this paper, we researched personalized recommendation based on knowledge graphs. First of all, we study the knowledge graphs’ construction method and complete the construction of the movie knowledge graphs. Furthermore, we use Neo4j graph database to store the movie data and vividly display it. Then, the classical translation model TransE algorithm in knowledge graph representation learning technology is studied in this paper, and we improved the algorithm through a cross-training method by using the information of the neighboring feature structures of the entities in the knowledge graph. Furthermore, the negative sampling process of TransE algorithm is improved. The experimental results show that the improved TransE model can more accurately vectorize entities and relations. Finally, this paper constructs a recommendation model by combining knowledge graphs with ranking learning and neural network. We propose the Bayesian personalized recommendation model based on knowledge graphs (KG-BPR) and the neural network recommendation model based on knowledge graphs(KG-NN). The semantic information of entities and relations in knowledge graphs is embedded into vector space by using improved TransE method, and we compare the results. The item entity vectors containing external knowledge information are integrated into the BPR model and neural network, respectively, which make up for the lack of knowledge information of the item itself. Finally, the experimental analysis is carried out on MovieLens-1M data set. The experimental results show that the two recommendation models proposed in this paper can effectively improve the accuracy, recall, F1 value and MAP value of recommendation.

Optimized Collaborative Filtering Recommendation Based on User’s Social Relationships

2014 ◽  
Vol 1044-1045 ◽  
pp. 1484-1488
Author(s):  
Yue Kun Fan ◽  
Xin Ye Li ◽  
Meng Meng Cao
Keyword(s):  
Collaborative Filtering ◽  
Social Relationships ◽  
Digital Libraries ◽  
Social Relationship ◽  
Nearest Neighbor ◽  
Mean Absolute Error ◽  
Absolute Error ◽  
Personalized Recommendation ◽  
Recommendation Algorithm

Currently collaborative filtering is widely used in e-commerce, digital libraries and other areas of personalized recommendation service system. Nearest-neighbor algorithm is the earliest proposed and the main collaborative filtering recommendation algorithm, but the data sparsity and cold-start problems seriously affect the recommendation quality. To solve these problems, A collaborative filtering recommendation algorithm based on users' social relationships is proposed. 0n the basis of traditional filtering recommendation technology, it combines with the interested objects of user's social relationship and takes the advantage of the tags to projects marked by users and their interested objects to improve the methods of recommendation. The experimental results of MAE ((Mean Absolute Error)) verify that this method can get better quality of recommendation.

scholarly journals A MODEL BASED ON FUZZY LINGUISTIC INFORMATION TO EVALUATE THE QUALITY OF DIGITAL LIBRARIES

2010 ◽  
Vol 09 (03) ◽  
pp. 455-472 ◽  
Author(s):  
F. J. CABRERIZO ◽  
J. LÓPEZ-GIJÓN ◽  
A. A. RUÍZ ◽  
E. HERRERA-VIEDMA
Keyword(s):  
Digital Libraries ◽  
User Satisfaction ◽  
Information Access ◽  
Great Influence ◽  
Linguistic Information ◽  
Model Based ◽  
Global Quality ◽  
Fuzzy Linguistic ◽  
The Web

The Web is changing the information access processes and it is one of the most important information media. Thus, the developments on the Web are having a great influence over the developments on others information access instruments as digital libraries. As the development of digital libraries is to satisfy user need, user satisfaction is essential for the success of a digital library. The aim of this paper is to present a model based on fuzzy linguistic information to evaluate the quality of digital libraries. The quality evaluation of digital libraries is defined using users' perceptions on the quality of digital services provided through their Websites. We assume a fuzzy linguistic modeling to represent the users' perception and apply automatic tools of fuzzy computing with words based on the LOWA and LWA operators to compute global quality evaluations of digital libraries. Additionally, we show an example of application of this model where three Spanish academic digital libraries are evaluated by fifty users.

QUALITY OF ECONOMIC AND PRODUCTION MANAGEMENT: PRACTICE, QUESTIONS OF THEORY

2021 ◽  
Vol 1 (8) ◽  
pp. 12-19
Author(s):  
V. G. VERSAN ◽  
Keyword(s):  
Economic Development ◽  
Production Management ◽  
Management Practice ◽  
Management Model ◽  
Global Trends ◽  
Model Based ◽  
Socio Economic Development ◽  
Practice Questions

The article notes the low quality of economic and production management in Russia. The reasons for this and ways to eliminate them are established. It is shown that the global trends of socio-economic development are not fully reflected in the theory of economic and production management. The ways of developing a management model based on improving the quality of interaction between people and economic entities are proposed. The concept of interaction in relation to socio-economic processes is revealed. The ways of minimizing management costs are considered.

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