Introduction
Background and related work
Recommender systems
Item | Relations | |
---|---|---|
Explicit | Implicit | |
Feedback provided by users | Users provide feedback directly. Private information leakage may occur | None |
Data accuracy | High | Low |
Gather user information | Low | High |
Computation load |
Recommendation technique | Advantages | Disadvantages |
---|---|---|
Content-based filtering | Explainable recommendations | New user problem Depends on historical data. Unable to make good recommendations when there is no sufficient historical data |
Collaborative filtering | Customized recommendations with the increasing number of users and items Higher recommendation accuracy over time | New user problem New item problem Depends on historical data. Unable to make good recommendations when there is no sufficient historical data |
Hybrid recommender | Solve new user and new item problem | More complex computing |
Cold-start problem
-
New community: when a new system launches, there may be many items in the catalog but little user interaction and presence, making it difficult to provide reliable recommendations.
-
New items: new items of the system, there may be relevant content information, but there is no user interaction.
-
New users: new users may enter the system without any interaction with the systems and without any personalized recommendations for the guests.
Brief intro to Web 2.0
Web 1.0 | Web 2.0 |
---|---|
DoubleClick | Google AdSense |
Ofoto | Flickr |
Akamai | BitTorrent |
mp3.com | Napster |
Britannica online | Wikipedia |
Personal websites | Blogging |
Evite | Upcoming.org and EVDB |
Domain name speculation | Search engine optimization |
Page views | Cost per click |
Screen scraping | Web services |
Publishing | Participation |
Content management systems | Wikis |
Directories (taxonomy) | Tagging (“folksonomy”) |
Really simple syndication (RSS)
Blog
Wiki
Social network
Social network
Description | |
---|---|
Advantages | Know more new friends quickly and easily. Able to see friends’ status updates in real-time |
Disadvantages | Information leakage may happen. Lack of identity authentication |
-
\(u_{e}\) (Affinity Score): how “connected” is a particular user to the edge?
-
\(w_{e}\) (Edge Weight): what actions were taken by the user on the content?
-
\(d_{e}\) (Time Decay): how old is the post?
-
\(I\) (Interest) = interest of the user in the creator.
-
P (Post) = the post’s performance amongst other users.
-
C (Creator) = performance of past posts by the content creator amongst other users.
-
T (Type) = types of posts (status, photos, links) that user prefers.
-
R (Recency) = how new the post is.
Yelp
Methods
Problem statement
System framework
Procedures
Analysis
Simulation design
Steps
-
With user information of interest,$${\text{I}}_{{\text{u}}} \left( c \right) = \frac{{n_{c} }}{{ \sum \nolimits_{{i = 1}}^{j} n_{i} }},\quad 1 \le i \le j,$$(2)
-
\({\text{n}}_{{\text{c}}}\): number of user comments in each category.
-
\(n_{i}\): number of users interested in this category.
-
\(j\): number of category.
-
-
Without user information of interest,$${\text{I}}_{{\text{u}}} \left( c \right) = \frac{{ \sum \nolimits_{{i = 1}}^{n} F_{i} \left( u \right) \times I_{{fi}} \left( c \right)}}{{ \sum \nolimits_{{i = 1}}^{n} F_{i} \left( u \right)}},\quad i \le n,$$(3)
-
\(I_{{fi}}\): level of interest for each category of user friend.
-
\(n\): number of users’ friends on Yelp.
-
\(F_{i} \left( u \right)\): user to friend interaction scores based on ay-fb-friend-rank algorithm [28].
-
-
\(I\left( k \right)\): level of interest for all categories.
Test steps
-
\(W\left( p \right)\): weight value of popularity.
-
\(\alpha\): weight value of acceptance.
-
\(P\left( p \right)\): popularity of the place.
-
\(\beta\): weight value of friends’ reviews.
-
F \(\left( p \right)\): place that has been visited by friends.
-
\(n_{{ch,p}}\): number of reviews for a location
-
\(n_{{li,p}}\): rating of a location$$F\left( p \right) = \frac{{n_{c} }}{{n_{s} }},$$(6)
-
\(n_{s}\): number of reviews for a location
-
\(n_{c}\): rating of a location
Evaluation metrics
Simulation data and results
-
Scenario 1: reviews submitted to Yelp from January 1, 2016 to December 31, 2016 were taken for simulation. Among those, we retrieved 20 reviews written by users who also wrote reviews between January 1, 2017 and December 22, 2017 to estimate accurate recommendations for new items.
-
Scenario 2: using Yelp review data from January 1, 2017 to December 22, 2017, 20 people who submitted reviews during the period were selected to estimate accuracy rate of new users.
Result and discussion
Scenario 1
User | MRR | User | MRR |
---|---|---|---|
1 | 1/4 | 11 | 1 |
2 | 1/2 | 12 | 1 |
3 | 1/2 | 13 | 1/3 |
4 | 1 | 14 | 1/3 |
5 | 1 | 15 | 1/2 |
6 | 1/5 | 16 | 1/3 |
7 | 1/2 | 17 | 1 |
8 | 1/2 | 18 | 1/2 |
9 | 1/4 | 19 | 1 |
10 | 1/2 | 20 | 1/5 |
User | MRR | User | MRR |
---|---|---|---|
1 | 1/3 | 11 | 1 |
2 | 1/2 | 12 | 1 |
3 | 1/2 | 13 | 1/3 |
4 | 1 | 14 | 1/3 |
5 | 1 | 15 | 1/2 |
6 | 1/5 | 16 | 1/3 |
7 | 1/3 | 17 | 1 |
8 | 1/2 | 18 | 1/2 |
9 | 1/4 | 19 | 1 |
10 | 1/2 | 20 | 1/3 |
User | MRR | User | MRR |
---|---|---|---|
1 | 1/3 | 11 | 1 |
2 | 1/2 | 12 | 1 |
3 | 1/2 | 13 | 1/3 |
4 | 1 | 14 | 1/2 |
5 | 1 | 15 | 1/2 |
6 | 1/5 | 16 | 1/2 |
7 | 1/3 | 17 | 1 |
8 | 1/2 | 18 | 1/2 |
9 | 1/4 | 19 | 1 |
10 | 1 | 20 | 1/4 |
User | MRR | User | MRR |
---|---|---|---|
1 | 1/3 | 11 | 1 |
2 | 1/2 | 12 | 1 |
3 | 1/2 | 13 | 1/3 |
4 | 1/2 | 14 | 1/2 |
5 | 1 | 15 | 1/2 |
6 | 1/5 | 16 | 1/2 |
7 | 1/3 | 17 | 1 |
8 | 1/3 | 18 | 1/3 |
9 | 1/4 | 19 | 1 |
10 | 1 | 20 | 1/4 |
User | MRR | User | MRR |
---|---|---|---|
1 | 1/3 | 11 | 1 |
2 | 1/2 | 12 | 1 |
3 | 1/2 | 13 | 1/3 |
4 | 1 | 14 | 1/3 |
5 | 1 | 15 | 1/2 |
6 | 1/5 | 16 | 1/2 |
7 | 1/3 | 17 | 1 |
8 | 1/2 | 18 | 1/2 |
9 | 1/4 | 19 | 1 |
10 | 1 | 20 | 1/4 |
Scenario 2
-
\(P\left( A \right)\): probability of an event,
-
\(n\): number of favorable outcomes,
-
\(t\): total number of possible outcomes.
User | MRR | User | MRR |
---|---|---|---|
1 | 1/3 | 11 | 1/3 |
2 | 1 | 12 | 1/5 |
3 | 1/2 | 13 | 1/3 |
4 | 1/5 | 14 | 1/4 |
5 | 1/3 | 15 | 1/3 |
6 | 1/2 | 16 | 1/4 |
7 | 1/3 | 17 | 1/4 |
8 | 1/4 | 18 | 1/5 |
9 | 1/3 | 19 | 1/3 |
10 | 1/3 | 20 | 1/4 |
User | MRR | User | MRR |
---|---|---|---|
1 | 1/3 | 11 | 1/3 |
2 | 1 | 12 | 1/5 |
3 | 1/2 | 13 | 1/3 |
4 | 1/5 | 14 | 1/4 |
5 | 1/3 | 15 | 1/3 |
6 | 1/2 | 16 | 1/4 |
7 | 1/3 | 17 | 1/4 |
8 | 1/4 | 18 | 1/5 |
9 | 1/3 | 19 | 1/3 |
10 | 1/3 | 20 | 1/4 |
User | MRR | User | MRR |
---|---|---|---|
1 | 1/3 | 11 | 1/3 |
2 | 1/2 | 12 | 1/5 |
3 | 1/4 | 13 | 1/2 |
4 | 1/5 | 14 | 1/4 |
5 | 1/3 | 15 | 1/3 |
6 | 1/2 | 16 | 1/5 |
7 | 1/3 | 17 | 1/3 |
8 | 1/4 | 18 | 1/5 |
9 | 1/3 | 19 | 1/3 |
10 | 1/4 | 20 | 1/4 |
User | MRR | User | MRR |
---|---|---|---|
1 | 1/3 | 11 | 1/3 |
2 | 1 | 12 | 1/5 |
3 | 1/3 | 13 | 1/3 |
4 | 1/5 | 14 | 1/4 |
5 | 1/3 | 15 | 1/3 |
6 | 1/2 | 16 | 1/4 |
7 | 1/3 | 17 | 1/3 |
8 | 1/4 | 18 | 1/5 |
9 | 1/3 | 19 | 1/3 |
10 | 1/3 | 20 | 1/4 |