Keyphrases
Recommender Systems
90%
Personality System
28%
Textual Reviews
28%
User Reviews
28%
Preference-based
27%
User Preference
26%
Product Reviews
22%
Electronic Commerce
21%
Contextual Information
21%
Pairwise Preference
17%
Collaborative Filtering
17%
Digital Camera
16%
User Study
15%
Recommendation Accuracy
15%
Rich Interaction
14%
One-class Collaborative Filtering
14%
Bayesian Personalized Ranking
14%
Overall Rating
14%
Sub-branch
14%
Product Profile
14%
Nonlinear State-space Model
14%
Personality Diversity
14%
Open Online Courses
14%
Compound Critiquing
14%
Bar Chart
14%
Review-based Recommender System
14%
Feature Extracting
14%
Cold-start Problem
14%
Machine Learning Techniques
14%
Feature Preferences
14%
User Experience
14%
Latent Class Regression
14%
Systems-based
14%
User Perception
14%
User Profile
14%
Personalized Recommendation
14%
Students At-risk
14%
Buyers
14%
Recommender
14%
Explaining Recommendations
14%
Explanation Interface
14%
Recommendation Diversity
14%
Implicit Feedback
14%
Recommendation Algorithm
14%
Collaborative Approach
14%
Perceived Information
13%
Conscientiousness
11%
System Recommendation
11%
User Behavior
10%
User-generated
10%
Within-subject Experiments
9%
Product Knowledge
9%
Purchase Intention
9%
Product Domains
9%
Information Usefulness
9%
Decision Environment
9%
Critiquing Systems
8%
Diverse Recommendations
7%
System Experiment
7%
Group Recommendation
7%
Social Media Behavior
7%
Social Media Stream
7%
Personality Models
7%
Mobile Phone Calls
7%
Call Logs
7%
Preference Similarity
7%
Group Preference
7%
Implicit Community
7%
Algorithm Development
7%
Systemability
7%
User Aspects
7%
Dropout Prediction
7%
Student Dropout
7%
Sequence Classification
7%
Context-dependent Preferences
7%
Product Ranking
7%
Or Comparative
7%
Rating Sparsity
7%
Art Studies
7%
Neuroticism
7%
Personality Factors
7%
Extraversion
7%
Personality Characteristics
7%
User Characteristics
7%
Five-factor Model
7%
Cross-domain
7%
Agreeableness
7%
Learning Styles
7%
Music Preference
7%
Behavior Learning
7%
Numerical Value
7%
Quality Assessment
7%
Dropout Rate
7%
Opinion Mining
7%
Rating Level
7%
Weighted Features
7%
Dropout
7%
Novel Clustering
7%
Superior Performance
7%
Latent State
7%
Aspect Level
7%
Contextual Preferences
7%
Mining Method
7%
User Modeling
7%
Clustering Methods
7%
Usage Experience
7%
Feature Level
7%
Cluster Level
7%
State-of-the-art Techniques
7%
Prediction Accuracy
7%
High Risk
7%
Value Preferences
7%
Text Analysis
7%
Student Learning Activities
7%
Iterative Process
7%
Prediction Problems
7%
Activity Logs
7%
Ranking Method
7%
Classification Problem
7%
Content-based
7%
Weight Value
7%
User Feedback
6%
Session Identification
5%
Recommendation Efficiency
5%
Graph-based
5%
Experience-based
5%
Computer Science
Recommender System
100%
User Preference
36%
Digital Camera
33%
Extracted Feature
28%
Collaborative Filtering
26%
Recommendation Accuracy
25%
Product Domain
19%
Contextual Information
19%
Pairwise Preference
19%
Clustering Method
14%
State Space
14%
Superior Performance
14%
Sentiment Score
14%
Alternative Design
14%
Cognitive Effort
14%
Customer Experience
14%
Perceived Certainty
14%
Iterative Process
14%
Usage Experience
14%
Conscientiousness
14%
Machine Learning Technique
14%
User Experience
14%
Positive Effect
14%
Decision Process
14%
Decision-Making
14%
Supporting User
14%
User Interface
14%
User Behavior
11%
Cold Start Problem
11%
Session Identification
9%
Comparative Opinion
9%
Numerical Value
7%
Research Direction
7%
Prediction Accuracy
7%
Learning Experiences
7%
Sequence Classification
7%
Individual User
7%
Classification Problem
7%
Activity Log
7%
Context Dependent
7%
Personality Factor
7%
Learning Style
7%
Recommendation Algorithm
7%