A Personalized Recommender System

2021 ◽  
pp. 32-42
Author(s):  
Akshit Nassa ◽  
◽  
Shubham Gupta ◽  
Pranjal Jindal ◽  
Achin Jain ◽  
...  
Keyword(s):  
Social Media ◽  
Recommender System ◽  
Consumer Preferences ◽  
Situational Factors ◽  
User Profiles ◽  
Previous Decade ◽  
Market Trends ◽  
Personalized Recommender System ◽  
Experimental Findings ◽  
The Impact

Due to social media, e-commerce, and the broader digitization of businesses, a data surge has occurred during the previous decade. The information is used to make informed decisions, forecast market trends, and identify patterns in consumer preferences. Following the widespread adoption of internet services, recommendation systems have become commonplace. The idea is to use filtering algorithms to recommend products to users who might be interested in them. Users are given recommendations for a media item such as movies by discovering user profiles of people who share similar interests. The preferences of users are first determined by allowing them to rate movies of their choosing. After some time, the recommender system will be able to better understand the user and recommend films that are more likely to get higher ratings. It also considers the impact of personal and situational factors on the user experience. In comparison to previous models, the experimental findings on the TMDB dataset provide a dependable model that is precise and generates more customized movie recommendations.

LOOKER: a mobile, personalized recommender system in the tourism domain based on social media user-generated content

2019 ◽  
Vol 23 (2) ◽  
pp. 181-197 ◽  
Author(s):  
Sondess Missaoui ◽  
Faten Kassem ◽  
Marco Viviani ◽  
Alessandra Agostini ◽  
Rim Faiz ◽  
...  
Keyword(s):  
Social Media ◽  
Recommender System ◽  
User Generated Content ◽  
Personalized Recommender System

Archetypal Personalized Recommender System for Mobile Phone Users

2013 ◽  
Vol 3 (3) ◽  
pp. 40-58 ◽  
Author(s):  
B. A. Ojokoh ◽  
M. O. Omisore ◽  
O. W. Samuel ◽  
U. I. Eno
Keyword(s):  
Mobile Phone ◽  
Recommender System ◽  
Consumer Preferences ◽  
Professional Services ◽  
Product Selection ◽  
Decision Systems ◽  
Product Features ◽  
Key Factor ◽  
Computer Based ◽  
Personalized Recommender System

The process of mobile phone selection, for several reasons, depends on a number of common individual features possessed by the manufacturers. The recent advance in these products’ functionalities is identified as a key factor for the growing number of brands and models that compete in its fierce market and thus leads to the problem of product selection. Product comparisons, as a result, are becoming more difficult thus favoring the use of computer-based decision systems to assist consumers in scouting for information on mobile products that can best satisfy their needs. This study proposes an archetypal personalized recommender system that can intelligently mine information about the features of mobile phones and provides professional services to potential buyers. Consumer preferences and product features are technically expressed with the aid of Triangular Fuzzy Numbers while Fuzzy Near Compactness is employed to measure the feature-need similarities in order to recommend optimal products that best satisfy the needs. Finally, an experimental study is performed to examine the feasibility and effectiveness of the proposed system.

Content analysis of pancreatic cancer conversations on Twitter: What matters most to users?

2020 ◽  
Vol 38 (15_suppl) ◽  
pp. 11040-11040
Author(s):  
Udhayvir Singh Grewal ◽  
Jamie Doggett ◽  
Emil Lou ◽  
Allyson J. Ocean ◽  
Niraj Jaysukh Gusani ◽  
...  
Keyword(s):  
Machine Learning ◽  
Health Care ◽  
Pancreatic Cancer ◽  
Social Media ◽  
Analytical Tool ◽  
User Profiles ◽  
Future Studies ◽  
Treatment Research ◽  
The Impact ◽  
Treatment Table

11040 Background: Social media has an important role in addressing medical misinformation by connecting the global community of health care professional (HCP), cancer patients and advocates. We evaluated the content and dynamics of discussions around pancreatic cancer (PC) on Twitter to identify subtopics of greatest interest to these users. Methods: We used online analytical tool (CREATION Pinpoint) to quantify Twitter mentions (tweets and re-tweets) related to PC between 1/2018 to 12/2019. Keywords, hashtags, word combinations and phrases were used to query for PC mentions. HCP profiles were identified using machine learning and then human verified and remaining user profiles were classified as general public (GP). Data from conversations were analysed and stratified qualitatively (using e.g keywords/combinations/phrases) into 5 categories; 1) prevention (P), 2) survivorship (S), 3) treatment (T), 4) research (R), and 5) policy (Po). We analysed the impact of PC awareness month (PCAM) and celebrity PC diagnosis on the overall level of conversations. Results: Out of 1,258,028 mentions on PC, 313,668 unique mentions were classified into the 5 categories. We found that HCP discuss PC research more than the GP, while GP are more interested in treatment. PCAM did not increase mentions by HCP in any of 5 categories while GP mentions over 2 years, increased temporarily in all categories except prevention. HCP mentions did not increase with celebrity PC diagnosis. Alex Trebek’s diagnosis increased GP mentions on survivorship, while Ruth Ginsburg’s diagnosis increased conversations on treatment (Table). Conclusions: Twitter mentions between HCP and GP around PC are not aligned. The HCP conversation was mainly limited to research while GP were more interested in treatment. PCAM temporarily increased GP conversations around treatment, research, survivorship and policy but not prevention. Future studies should address which factors determine how celebrity diagnoses drive conversations. [Table: see text]

The impact of language style accommodation during social media interactions on brand trust

2017 ◽  
Vol 28 (3) ◽  
pp. 418-441 ◽  
Author(s):  
Ana Jakic ◽  
Maximilian Oskar Wagner ◽  
Anton Meyer
Keyword(s):  
Social Media ◽  
Laboratory Experiment ◽  
Situational Factors ◽  
Brand Trust ◽  
Interpersonal Interactions ◽  
Communication Accommodation ◽  
Content Type ◽  
Language Style ◽  
Accommodation Theory ◽  
The Impact

Purpose Social media encourage interactions between customers and brands. Concerning the cues utilized during social media interactions, verbal cues (i.e. the language used) gain importance, since non-verbal and paraverbal cues are hard to convey via social media. Looking at interpersonal interactions, interlocutors adopt each other’s language styles or maintain their own language style during interactions to build trust. Transferring these insights to social media, the purpose of this paper is to test the effects of a brand’s language style accommodation in brand-customer interactions on brand trust and on its antecedents. Design/methodology/approach Two quantitative pre-studies (n1 (questionnaire)=32, n2 (laboratory experiment)=199), and one quantitative main study (n3 (laboratory experiment)=427) were conducted to determine the effects of a brand’s language style accommodation on brand trust. Findings In line with communication accommodation theory, this paper reveals that the impact of a brand’s accommodation strategy on brand trust is mediated by perceived relationship investments, such as perceived interaction effort, benevolence, and quality of interaction. This paper also underscores language style’s roles and its fit, and sheds light on situational factors such as purchase decision involvement and the valence of the content. Originality/value This paper is the first to transfer cross-disciplinary theories on interpersonal interactions to brand-customer interactions in social media. Thus, the authors derive the effects of language style accommodation on brand trust as well as further mediating effects.

scholarly journals A Time-Aware CNN-Based Personalized Recommender System

Complexity ◽  
2019 ◽  
Vol 2019 ◽  
pp. 1-11 ◽  
Author(s):  
Dan Yang ◽  
Jing Zhang ◽  
Sifeng Wang ◽  
XueDong Zhang
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Recommender System ◽  
Learning Algorithms ◽  
Large Data ◽  
Depth Study ◽  
Data Volume ◽  
Personalized Recommender System ◽  
The Impact ◽  
Time Aware

Recommender system has received tremendous attention and has been studied by scholars in recent years due to its wide applications in different domains. With the in-depth study and application of deep learning algorithms, deep neural network is gradually used in recommender systems. The success of modern recommender system mainly depends on the understanding and application of the context of recommendation requests. However, when leveraging deep learning algorithms for recommendation, the impact of context information such as recommendation time and location is often neglected. In this paper, a time-aware convolutional neural network- (CNN-) based personalized recommender system TC-PR is proposed. TC-PR actively recommends items that meet users’ interests by analyzing users’ features, items’ features, and users’ ratings, as well as users’ time context. Moreover, we use Tensorflow distributed open source framework to implement the proposed time-aware CNN-based recommendation algorithm which can effectively solve the problems of large data volume, large model, and slow speed of recommender system. The experimental results on the MovieLens-1m real dataset show that the proposed TC-PR can effectively solve the cold-start problem and greatly improve the speed of data processing and the accuracy of recommendation.

scholarly journals The Use of Social Media and Its Impact on Shopping Behavior of Slovak and Italian Consumers during COVID-19 Pandemic

2021 ◽  
Vol 13 (4) ◽  
pp. 1710
Author(s):  
Viktória Ali Taha ◽  
Tonino Pencarelli ◽  
Veronika Škerháková ◽  
Richard Fedorko ◽  
Martina Košíková
Keyword(s):  
Social Media ◽  
Consumer Preferences ◽  
Rank Correlation ◽  
Shopping Behavior ◽  
Spearman’S Rank Correlation ◽  
Spearman’S Rank Correlation Coefficient ◽  
The Social ◽  
And Gender ◽  
Use Of Social Media ◽  
The Impact

The coronavirus crisis hit the world and affected all aspects of our lives, including consumers’ habits, preferences, and shopping behaviors. The survey, which involved 937 respondents from two countries, examined how the pandemic affected shopping behavior and consumer preferences in Italy and Slovakia. This paper aims to explore the impact of social media on consumer behavior, more specifically, it examines the influence of social media on the preference of specific e-shops during the first wave of the COVID-19 pandemic. Spearman’s rank correlation coefficient was used to determine a statistically significant relationship between the variables and the Mann–Whitney U test and the Kruskal–Wallis H test to assess the significance of differences between respondents in terms of demographic characteristics (residence, age, and gender). The results revealed the existence of statistically significant differences in the use of social media during the first wave of the COVID-19 pandemic in terms of various demographic factors as well as a relatively weak relationship between the social media used and the purchase in the e-shop promoted on the social media.

scholarly journals Dual Co-Attention-Based Multi-Feature Fusion Method for Rumor Detection

Information ◽  
2022 ◽  
Vol 13 (1) ◽  
pp. 25
Author(s):  
Changsong Bing ◽  
Yirong Wu ◽  
Fangmin Dong ◽  
Shouzhi Xu ◽  
Xiaodi Liu ◽  
...  
Keyword(s):  
Neural Network ◽  
Social Media ◽  
Feature Fusion ◽  
Instant Messaging ◽  
Attention Mechanism ◽  
Fusion Method ◽  
User Profiles ◽  
Hidden Layer ◽  
Rumor Detection ◽  
The Impact

Social media has become more popular these days due to widely used instant messaging. Nevertheless, rumor propagation on social media has become an increasingly important issue. The purpose of this study is to investigate the impact of various features in social media on rumor detection, propose a dual co-attention-based multi-feature fusion method for rumor detection, and explore the detection capability of the proposed method in early rumor detection tasks. The proposed BERT-based Dual Co-attention Neural Network (BDCoNN) method for rumor detection, which uses BERT for word embedding . It simultaneously integrates features from three sources: publishing user profiles, source tweets, and comments. In the BDCoNN method, user discrete features and identity descriptors in user profiles are extracted using a one-dimensional convolutional neural network (CNN) and TextCNN, respectively. The bidirectional gate recurrent unit network (BiGRU) with a hierarchical attention mechanism is used to learn the hidden layer representation of tweet sequence and comment sequence. A dual collaborative attention mechanism is used to explore the correlation among publishing user profiles, tweet content, and comments. Then the feature vector is fed into classifier to identify the implicit differences between rumor spreaders and non-rumor spreaders. In this study, we conducted several experiments on the Weibo and CED datasets collected from microblog. The results show that the proposed method achieves the state-of-the-art performance compared with baseline methods, which is 5.2% and 5% higher than the dEFEND. The F1 value is increased by 4.4% and 4%, respectively. In addition, this paper conducts research on early rumor detection tasks, which verifies the proposed method detects rumors more quickly and accurately than competitors.

INFLUENCE OF SITUATION FACTORS ON CONSUMER BEHAVIOR

2017 ◽  
Author(s):  
Viktoriia Traino ◽  
Keyword(s):  
Consumer Behavior ◽  
Consumer Preferences ◽  
Customer Behavior ◽  
Situational Factors ◽  
State And Society ◽  
Psychological Differences ◽  
Analysis And Synthesis ◽  
General Scientific ◽  
Effective Instrument ◽  
The Impact

In competitive markets, it is important for companies to identify factors that affect consumer behavior and well-timed anticipate alterations in their behavior. The probability of purchasing goods or services by the consumer depends on certain situations in which he finds himself. Understanding the characteristics of situational factors, consumer preferences, habits, the ability to predict and manage them will influence consumer behavior, especially in the buying process. At present, the issues of studying the influence of situational factors on consumer behavior in today's unique conditions are insufficiently studied. The purpose of the publication is to analyze the impact of situational factors on consumer behavior in today's special conditions and to propose appropriate measures to improve the activities of enterprises, taking into account the factors considered. The study used general scientific research methods: abstract-logical, analysis and synthesis, systematization and generalization. Consumer behavior is a person's actions under the influence of the environment, individual and psychological differences in the process of realizing the need, finding information, choosing, purchasing, using goods or services and getting rid of them. An important condition for the effective operation of the enterprise is to determine the most noteworthy factors influencing consumer behavior. One of such factors is situational. The modern period is characterized by the following situations that regulate or modification the habits of consumers: changes in social status; emergence of the latest technologies; rules and norms established by the state and society; emergencies. During the life of the same person under the influence of situations modification desires, values, habits, needs, behavior. Therefore, businesspersons and marketers should allot special attention to situational factors that influence consumer behavior. After all, the influence on customer behavior is a effective instrument for modeling consumer behavior and demand governance.

scholarly journals The Impact of Personality and Situational Factors on Perceived Stress: An Investigation During COVID-19 Pandemic

2021 ◽  
Author(s):  
Ngoc Do
Keyword(s):  
At Risk ◽  
Social Media ◽  
Personality Traits ◽  
Perceived Stress ◽  
Stress Level ◽  
Personal Finance ◽  
Situational Factors ◽  
The Impact ◽  
The Relationship ◽  
Moderate Stress

The study investigates the relationship between personality and situational factors on perceived stress level during the global COVID-19 pandemic. Analysis of data collected from people across different territories confirms the association between personality traits and perceived stress level. Furthermore, the paper shows that people are experiencing moderate stress, which is affected by where they are residing, whether their personal finance is at risk, and their usage of social media during the pandemic.

Sign in / Sign up

Export Citation Format

Share Document