This document proposes a novel weak supervision approach to unify explicit and implicit feedback for rating prediction and ranking recommendation tasks. It trains an explicit feedback model to annotate implicit feedback with predicted ratings. This allows training a new model on the annotated data, improving ranking accuracy while increasing coverage of long-tail items compared to baselines. Evaluation on multiple datasets shows the approach enhances recommendation for both rating prediction and ranking, with less popularity bias than models using only explicit or implicit feedback.

1. Unifying Explicit and Implicit Feedback for
Rating Prediction and Ranking
Recommendation Tasks
Amir H. Jadidinejad, Craig Macdonald, Iadh Ounis
University of Glasgow
ICTIR 2019

2. Introduction
When users visit recommendation websites
they generate two types of feedback
• Explicit Feedback: The user is consciously
providing information to the system
• Implicit Feedback: The system is collecting
feedback based on users behaviour (invisibly
to them)
These two types of feedback can be used to
automate recommendation tasks
• Explicit → Rating Prediction (e.g. MF)
• Implicit → Ranking (e.g. BPR)
2
Rating Reviews Likes
Clicks

3. Motivation
3
M. Wan and J. McAuley. Item Recommendation on Monotonic Behavior Chains, RecSys 2018.
We also aim to unify explicit and implicit feedback in order to
bridge the rating prediction and ranking prediction tasks
click purchase review recommend
feedback type

4. Our Approach wrt Existing
Literature
Previous unifying approaches tackled the problem at the level of
recommendation model
• i.e. they proposed new models to combine both explicit and implicit
feedback
The main barrier in unifying explicit and implicit feedback is that
they are heterogeneous in terms of:
• Representations: <u,i,r> vs <u,i>
• Distributions: 90% clicks, 10% reviews
4
Instead, we propose a novel weak supervision approach that bridges the gap
between explicit and implicit feedback at the level of data preprocessing.

5. Previous Unifying Approaches
Models that leverage a shared representation of users and items
• Joint Representation Learning for Top-N Recommendation with
Heterogeneous Information Sources (Zhang et al. CIKM 2017)
• Personalized Ranking Recommendation via Integrating Multiple Feedbacks
(Liu et al. PAKDD 2017)
Models that extend existing recommendation algorithms
• Item Recommendation on Monotonic Behavior Chains. In Proceedings of
(Wan and McAuley. RecSys 2018)
• Bayesian Personalized Ranking with Multi-Channel User Feedback (Loni et
al. RecSys 2016)
5

6. Our Proposed Approach (1)
6
Our Proposal: Transfer the knowledge of rating prediction
from explicit feedback to annotate implicit feedback
𝐷𝑖 = ⟨𝑈, 𝐼⟩
Idea: Can we use explicit feedback De to infer the user ratings of Di?
𝐷𝑖
∗
= ⟨𝑈, 𝐼, 𝑅∗⟩

7. Our Proposed Approach (2)
1. Train a model based on explicit
feedback (ratings) :
7
Ratings
Clicks

8. Our Proposed Approach (2)
1. Train a model based on explicit
feedback (ratings) :
2. Use the model from (1) to annotate
implicit (click) feedback by predicting
the rating values -> :
3. Train a new model based on the
annotated dataset :
8
Ratings
Predicted
ratings
for clicks

9. Features of our Unifying
Approach
Approach
Rating
Prediction
Ranking Long-tail
Coverage
Explicit Models
(MF)
Implicit Models
(BPR)
Our Approach
9
Our aim is to improve the capability of the explicit models for the task of ranking
through the use of a novel weak supervision approach that unifies both explicit
and implicit feedback datasets

10. Research Questions
• RQ1: Does the proposed weak supervision approach
enhance recommendation accuracy for
(a) Rating Prediction
(b) Ranking
• RQ2: How much popularity bias is exhibited by our
proposed weak supervision approach compared with
the other baseline recommendation approaches?
10

11. Datasets
11
• GoodReads: Large book ratings dataset
• Steam: Video game reviews
• Douban: Chinese movie, book and music ratings
• Dianping: User ratings from DianPing.com

12. Evaluation Metrics
RQ1:
• Rating Prediction:
- Root Mean Square Error (RMSE)
• Ranking:
- MRR, nDCG, MAP
RQ2:
• Long-tail item coverage:
- Average Recommendation Popularity
(ARP) by Yin et al. (VLDB12):
12
Reviews
80/10/10
Clicks

13. Baselines
1. A popularity model
2. An explicit model
- Matrix Factorization:
3. An implicit model
- Bayesian Personalized Ranking:
4. An existing unifying model
- ChainRec (Wan and McAuley, RecSys 2018)
13

14. 14
RQ1.a
Φ 𝐷 𝑒
~ ΦDi
∗
RQ1.b
ΦDi
∗
>> Φ 𝐷 𝑒
𝑀𝐹𝐵𝑃𝑅 > ΦDi
∗
RQ2
ΦDi
∗
>> 𝑀𝐹𝐵𝑃𝑅
The proposed weak
supervision approach
improves the capability of
the explicit models for the
task of ranking (5 out of 6
datasets) and has a
better coverage of tail
items than BPR.

15. Qualitative Analysis
15
Over 70% of BPR’s recommended
items are among the top-20 most
popular items
Only 15% of the explicit
recommendations are among the top-20
most popular items (long tail coverage)

16. Conclusions and Future Work
Our approach unifies both the Rating prediction (explicit feedback)
and Ranking (implicit feedback) tasks while alleviating the bias
against less popular long-tail items.
• It is applied at the level of data pre-processing as a weak supervision
signal.
Future Work
• Combining weak supervision into pairwise approaches such as BPR or
neural network-based recommenders
• Tradeoff between good quality recommendations and long-tail item
coverage requires more investigation.
16

17. 17
Thanks!
• Data and code is available at:
- https://github.com/amirj/unifying_explicit_implicit