Improving Recommender Systems Using Co-Appearing and Semantically Correlated User Interests

2020 ◽  
Vol 13 (2) ◽  
pp. 240-247 ◽  
Author(s):  
Bilal Hawashin ◽  
Darah Aqel ◽  
Shadi Alzubi ◽  
Mohammad Elbes
Keyword(s):  
Recommender Systems ◽  
Experimental Work ◽  
Execution Time ◽  
Word Embeddings ◽  
User Interest ◽  
User Interests ◽  
And Behavior ◽  
The Given

Background: Recommender Systems use user interests to provide more accurate recommendations according to user actual interests and behavior. Methods: This work aims at improving recommender systems by discovering hidden user interests from the existing interests. User interest expansion would contribute in improving the accuracy of recommender systems by finding more user interests using the given ones. Two methods are proposed to perform the expansion: Expanding interests using correlated interests’ extractor and Expanding interests using word embeddings. Results: Experimental work shows that such expanding is efficient in terms of accuracy and execution time. Conclusion: Therefore, expanding user interests proved to be a promising step in the improvement of the recommender systems performance.

Two Phase Recommendation Algorithm Based on Clustering User Interest and Trust

2014 ◽  
Vol 543-547 ◽  
pp. 1856-1859
Author(s):  
Xiang Cui ◽  
Gui Sheng Yin
Keyword(s):  
Social Networks ◽  
Electronic Commerce ◽  
Recommender Systems ◽  
Information Overload ◽  
User Interest ◽  
Recommendation Algorithm ◽  
Two Phase ◽  
Rating Data ◽  
User Interests ◽  
The Future

Recommender systems have been proven to be valuable means for Web online users to cope with the information overload and have become one of the most powerful and popular tools in electronic commerce. We need a method to solve such as what items to buy, what music to listen, or what news to read. The diversification of user interests and untruthfulness of rating data are the important problems of recommendation. In this article, we propose to use two phase recommendation based on user interest and trust ratings that have been given by actors to items. In the paper, we deal with the uncertain user interests by clustering firstly. In the algorithm, we compute the between-class entropy of any two clusters and get the stable classes. Secondly, we construct trust based social networks, and work out the trust scoring, in the class. At last, we provide some evaluation of the algorithms and propose the more improve ideas in the future.

Clarifying Ambiguous Keywords with Personal Word Embeddings for Personalized Search

2022 ◽  
Vol 40 (3) ◽  
pp. 1-29
Author(s):  
Jing Yao ◽  
Zhicheng Dou ◽  
Ji-Rong Wen
Keyword(s):  
Large Scale ◽  
Training Model ◽  
Search Problem ◽  
Word Embeddings ◽  
Information Need ◽  
User Interest ◽  
Personalized Search ◽  
Individual User ◽  
User Interests ◽  
Matching Score

Personalized search tailors document ranking lists for each individual user based on her interests and query intent to better satisfy the user’s information need. Many personalized search models have been proposed. They first build a user interest profile from the user’s search history, and then re-rank the documents based on the personalized matching scores between the created profile and candidate documents. In this article, we attempt to solve the personalized search problem from an alternative perspective of clarifying the user’s intention of the current query. We know that there are many ambiguous words in natural language such as “Apple.” People with different knowledge backgrounds and interests have personalized understandings of these words. Therefore, we propose a personalized search model with personal word embeddings for each individual user that mainly contain the word meanings that the user already knows and can reflect the user interests. To learn great personal word embeddings, we design a pre-training model that captures both the textual information of the query log and the information about user interests contained in the click-through data represented as a graph structure. With personal word embeddings, we obtain the personalized word and context-aware representations of the query and documents. Furthermore, we also employ the current session as the short-term search context to dynamically disambiguate the current query. Finally, we use a matching model to calculate the matching score between the personalized query and document representations for ranking. Experimental results on two large-scale query logs show that our designed model significantly outperforms state-of-the-art personalization models.

THE FEATURES OF FEMALE IMAGES IN THE TRAGEDIES OF GUSEYN JAVID

2020 ◽  
Vol 5 (3) ◽  
pp. 180-188
Author(s):  
Malika Adigezalova ◽  
Keyword(s):  
Female Characters ◽  
Female Images ◽  
Female Figures ◽  
Characteristic Features ◽  
And Behavior ◽  
The Given

The article is devoted to the features of female types in the tragedies of one of significant playwrights of the XX century Guseyn Javid. In the given article, they analyse and compare the characteristic features and behavior of the female figures of the author’s such literaryworks as «Mother»(Selma, Ismet), «Maral»(Maral, Humay), «Afet»(Afet, Alagoz), «Siyavush»(Farangiz, Sudaba). The basis of the article lies in the creative works of G.Javid, where special attention is attracted by several types of female characters, among which the types of a traditional eastern woman are most brightly represented

Discovery and classification of user interests on social media

2017 ◽  
Vol 45 (3) ◽  
pp. 130-138 ◽  
Author(s):  
Basit Shahzad ◽  
Ikramullah Lali ◽  
M. Saqib Nawaz ◽  
Waqar Aslam ◽  
Raza Mustafa ◽  
...  
Keyword(s):  
Information Filtering ◽  
Recommendation Systems ◽  
Personalized Recommendation ◽  
Support Vector ◽  
User Interest ◽  
Socioeconomic Impacts ◽  
Content Type ◽  
Positive Correlation ◽  
User Interests ◽  
Twitter Users

Purpose Twitter users’ generated data, known as tweets, are now not only used for communication and opinion sharing, but they are considered an important source of trendsetting, future prediction, recommendation systems and marketing. Using network features in tweet modeling and applying data mining and deep learning techniques on tweets is gaining more and more interest. Design/methodology/approach In this paper, user interests are discovered from Twitter Trends using a modeling approach that uses network-based text data (tweets). First, the popular trends are collected and stored in separate documents. These data are then pre-processed, followed by their labeling in respective categories. Data are then modeled and user interest for each Trending topic is calculated by considering positive tweets in that trend, average retweet and favorite count. Findings The proposed approach can be used to infer users’ topics of interest on Twitter and to categorize them. Support vector machine can be used for training and validation purposes. Positive tweets can be further analyzed to find user posting patterns. There is a positive correlation between tweets and Google data. Practical implications The results can be used in the development of information filtering and prediction systems, especially in personalized recommendation systems. Social implications Twitter microblogging platform offers content posting and sharing to billions of internet users worldwide. Therefore, this work has significant socioeconomic impacts. Originality/value This study guides on how Twitter network structure features can be exploited in discovering user interests using tweets. Further, positive correlation of Twitter Trends with Google Trends is reported, which validates the correctness of the authors’ approach.

scholarly journals COMMUNICATING MORALITY TO AUDIENCES: SYMBOLIC INTERACTION IN FILMS

2021 ◽  
Vol 17 (2 (24)) ◽  
pp. 92-99
Author(s):  
Gaiane Muradian
Keyword(s):  
Symbolic Interactionism ◽  
Research Methodology ◽  
Symbolic Interaction ◽  
Film Theory ◽  
Modern Theory ◽  
Film Version ◽  
And Behavior ◽  
The Given ◽  
Game Of Thrones

Through research methodology of case study the present paper promotes the connection between Symbolic Interactionism and film theory, the symbolic film world and the audience’s emotions, intellect and behavior. My purpose of focusing on the modern theory of Symbolic Interactionism is justified by the notion that the mentioned theory provides an ideal concept to achieve the objective of shaping the perceptions of massive audiences into possible positive directions, creating shared positive symbols in the society and making people react to the given symbols accordingly via film media. The analysis of the film version (A Game of Thrones) of George R. R. Martin’s series of epic fantasy novel A Song Of Ice and Fire (1996-2019) discloses the firm connection between Symbolic Interactionism and film theory, between symbolic interaction and communicating morality to audiences.

scholarly journals Perumusan dan Pengembangan Tujuan Instruksional Khusus di SDN Sarakan 2

AS-SABIQUN ◽  
2020 ◽  
Vol 2 (2) ◽  
pp. 66-82
Author(s):  
Ina Magdalena ◽  
Tasya Farlidya ◽  
Winda Yuniar
Keyword(s):  
Teaching Method ◽  
Instructional Management ◽  
Primary School Teachers ◽  
Equipment And Supplies ◽  
Instructional Objectives ◽  
Lecture Method ◽  
Grade 3 ◽  
Homeroom Teachers ◽  
And Behavior ◽  
The Given

This study aims to determine the writing of specific instructional objectives at SDN SARAKAN 2. The method that will be used at the time of research is descriptives method through a qualitative approach. The object to be studied is a grade 3 teacher at SDN SARAKAN 2. The process to be used at this time is interview and documentation techniques. The formulation of specific learning objectives includes only learners and behavior. The teaching method in genral the teacher applies the lecture method using the powerpoint slide media which takes place when online learning begins and online question and answer wuestions are the given online and the assignment is collected. Special instructional management is manidested in the presence of a coordinator who is responsible for equipment and supplies such as primary school teachers or commonly referred to as homeroom teachers, who have design favtors, namely: 1) Espertise in designing online-based learning; 2) Realization of teacher training; 3) The skill of applying concepts to new situations in different ways; 4) Make the school a learning organization; 5) The school is able to manage in planning, organizing, coordinating and supervising; 6) The ability to solve problems with systematic step in learning; 7) Skill to elaborate and evaluate learning outcomes.

Review-Based Recommender Systems: A Proposed Rating Prediction Scheme Using Word Embedding Representation of Reviews

2020 ◽  
Author(s):  
S Hasanzadeh ◽  
S M Fakhrahmad ◽  
M Taheri
Keyword(s):  
Vector Space ◽  
Recommender Systems ◽  
Recommender System ◽  
Word Embedding ◽  
Bag Of Words ◽  
Prediction System ◽  
User Reviews ◽  
Prediction Scheme ◽  
Rating Prediction ◽  
The Given

Abstract Recommender systems nowadays play an important role in providing helpful information for users, especially in ecommerce applications. Many of the proposed models use rating histories of the users in order to predict unknown ratings. Recently, users’ reviews as a valuable source of knowledge have attracted the attention of researchers in this field and a new category denoted as review-based recommender systems has emerged. In this study, we make use of the information included in user reviews as well as available rating scores to develop a review-based rating prediction system. The proposed scheme attempts to handle the uncertainty problem of the rating histories, by fuzzifying the given ratings. Another advantage of the proposed system is the use of a word embedding representation model for textual reviews, instead of using traditional models such as binary bag of words and TFIDF 1 vector space. It also makes use of the helpfulness voting scores, in order to prune data and achieve better results. The effectiveness of the rating prediction scheme as well as the final recommender system was evaluated against the Amazon dataset. Experimental results revealed that the proposed recommender system outperforms its counterparts and can be used as a suitable tool in ecommerce environments.

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