=Paper=
{{Paper
|id=Vol-1247/recsys14_poster10
|storemode=property
|title=Correcting Popularity Bias by Enhancing Recommendation Neutrality
|pdfUrl=https://ceur-ws.org/Vol-1247/recsys14_poster10.pdf
|volume=Vol-1247
|dblpUrl=https://dblp.org/rec/conf/recsys/KamishimaAAS14
}}
==Correcting Popularity Bias by Enhancing Recommendation Neutrality==
https://ceur-ws.org/Vol-1247/recsys14_poster10.pdf
Correcting Popularity Bias
by Enhancing Recommendation Neutrality
Toshihiro Kamishima, Shotaro Akaho, Jun Sakuma
and Hideki Asoh University of Tsukuba
National Institute of Advanced Industrial Science 1-1-1 Tennodai, Tsukuba, 305-8577 Japan
and Technology (AIST) jun@cs.tsukuba.ac.jp
AIST Tsukuba Central 2, Umezono 1-1-1,
Tsukuba, Ibaraki, 305-8568 Japan
mail@kamishima.net,
s.akaho@aist.go.jp, h.asoh@aist.go.jp
ABSTRACT INRS to avoid a well-known popularity bias, which is the
In this paper, we attempt to correct a popularity bias, which tendency for popular items to be recommended more fre-
is the tendency for popular items to be recommended more quently [1]. When users have no interest in the popularity
frequently, by enhancing recommendation neutrality. Rec- of items and wish to ignore this information, they can obtain
ommendation neutrality involves excluding specified infor- recommendations that are neutral with respect to the popu-
mation from the prediction process of recommendation. This larity of items by specifying the volume of their consumption
neutrality was formalized as the statistical independence be- as a viewpoint.
tween a recommendation result and the specified informa- The popularity bias has previously been corrected by di-
tion, and we developed a recommendation algorithm that versifying recommended items [5]. Specifically, instead of
satisfies this independence constraint. We correct the popu- the most popular and preferred items, slightly less preferred
larity bias by enhancing neutrality with respect to informa- and diverse kind of items are recommended. This diversifi-
tion regarding whether candidate items are popular or not. cation approach is different from our approach of enhancing
We empirically show that a popularity bias in the predicted recommendation neutrality. While diversity is a property of
preference scores can be corrected. a set of recommendations, neutrality is a relation between
recommendations and a specified viewpoint. Many notions
of diversity have been proposed, but all of them target a set
Keywords of recommendations; thus, it is impossible to correct a bias
recommender system, neutrality, fairness, popularity bias, with a single recommendation. On the other hand, a single
probabilistic matrix factorization, information theory recommendation can be neutral in its prediction of ratings
with respect to a specified viewpoint. This is useful, for ex-
ample, when attaching a list of items with predicted ratings
1. RECOMMENDATION NEUTRALITY that match a user’s query. Therefore, our INRS can be used
AND POPULARITY BIAS for correcting the popularity bias in each predicted score.
We proposed the notion of recommendation neutrality with
respect to a specified viewpoint if no information about the
viewpoint is exploited when generating the recommendation 2. EXPERIMENTS
results [3]. If we use terms of information theory, this notion
can be formalized as the condition that the mutual informa- We applied our INRS, mean-match [4], to show that our
tion between a recommendation result and a viewpoint is approach is effective in correcting a popularity bias. Sim-
zero, and it further implies statistical independence between ply speaking, this algorithm is a variant of the probabilistic
them. We developed information-neutral recommender sys- matrix factorization model [6] that adopts a constraint term
tems (INRS) that predict users’ preference scores while sat- for enhancing neutrality.
isfying the constraint of statistical independence [3, 4]. This We evaluated our experimental results in terms of predic-
INRS could be useful for the avoidance of biased recommen- tion errors and degree of neutrality. Prediction errors were
dation, fair treatment of content providers, or adherence to measured by the mean absolute error (MAE). This index
laws and regulations. In this paper, we use the proposed was defined as the mean of the absolute difference between
the observed rating values and predicted rating values. A
smaller value of this index indicates better prediction accu-
racy. To measure the degree of neutrality, we adopted nor-
malized mutual information (NMI) [4]. The NMI is defined
as mutual information between the predicted ratings and
viewpoint values, normalized into the range [0, 1]. A smaller
NMI indicates a higher level of neutrality. Note that the dis-
tribution of scores is modeled by a multinomial distribution
Copyright is held by the author/owner(s).
RecSys 2014 Poster Proceedings, October 6–10, 2014, Foster City, Silicon after discretizing prediction scores. We performed a five-fold
Valley, USA. cross-validation procedure to obtain evaluation indices.
0.01
0.70
0.005
MAE
NMI
0.65
0.002
0.60
0.001 dislike like dislike like
0.01 0.1 1 10 100 0.01 0.1 1 10 100
η η
(a) Prediction error (MAE) (b) Degree of neutrality (NMI) (a) standard (b) neutrality enhanced
Figure 1: Changes in the accuracy and degree of neutrality Figure 2: Distribution of the predicted ratings for short-head
accompanying an increase in the neutrality parameter and long-tail items
highly rated. After correcting the popularity bias (η = 100)
The data set was the Flixster data set1 [2]. The total
as in Figure 2(b), the distributions of ratings for short-head
numbers of users and movies were 147,612 and 48,794, re-
and long-tail items become much closer; that is to say, the
spectively, and the data set consisted of 8,196,077 ratings.
predicted ratings are less influenced by items’ popularity. It
Ratings are represented by a ten-point-scale whose domain
follows from this figure that our INRS successfully corrected
is 0.5 to 5.0 in 0.5 increments. To correct a popularity bias,
a popularity bias.
we adopted the popularity of items as a viewpoint. Candi-
date movies were first sorted by the number of users who
rated the movie in a descending order, and a viewpoint rep- 3. CONCLUSIONS
resented whether or not a movie was in the top 1% of this We corrected a popularity bias by enhancing recommen-
list. We called the group of top 1% items the short-head dation neutrality and empirically showed the effectiveness
items, and the group containing the rest the long-tail items. of our approach. We plan to improve the efficiency of our
Figure 1(a) shows the change of prediction errors mea- information-neutral recommendation algorithm and to adopt
sured by the MAE in a linear scale. Figure 1(b) shows a more sophisticated model for expressing popularity.
the change in NMI in a logarithmic scale. The X-axes of
these figures represent the values of a neutrality parameter, 4. ACKNOWLEDGMENTS
η, which balances the prediction of accuracy and neutral-
We would like to thank for providing a data set
ity. These parameters were changed from 0.01, at which the
for Dr. Mohsen Jamali. This work is supported
neutrality term was almost completely ignored, to 100, at
by MEXT/JSPS KAKENHI Grant Number 16700157,
which neutrality was strongly enhanced.
21500154, 24500194, and 25540094.
We first compared these with two baseline results. The
MAE was 0.871 when the rating being offered was held con-
stant at 3.61, which is the mean rating over all sample rat- 5. REFERENCES
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1
http://www.sfu.ca/~sja25/datasets/