=Paper=
{{Paper
|id=Vol-1440/paper8
|storemode=property
|title=An Adaptive Technique for Weighting Multiple Factors
in Followee Recommendation Algorithms
|pdfUrl=https://ceur-ws.org/Vol-1440/Paper8.pdf
|volume=Vol-1440
|dblpUrl=https://dblp.org/rec/conf/ijcai/TommaselG15a
}}
==An Adaptive Technique for Weighting Multiple Factors
in Followee Recommendation Algorithms ==
https://ceur-ws.org/Vol-1440/Paper8.pdf
An Adaptive Technique for Weighting Multiple Factors in Followee
Recommendation Algorithms
Antonela Tommasel and Daniela Godoy
ISISTAN Research Institute, CONICET-UNCPBA, Tandil, Buenos Aires, Argentina
Abstract Then, percentages are used as the similarity weights that will
The accurate suggestion of interesting friends arises as a be further updated according to user preferences.
crucial issue in recommendation systems. This work argues Updating Factor Weights. The computed weights are used for
that the criteria for recommending friends (or followees) needs assessing the similarity between each potential followee and
to be adapted and combined according to each user’s prefer- the target user in the recommendation process. The target user
ences. A technique is proposed for adapting such criteria to the is presented with the set of most similar potential followees.
characteristics of previously selected followees. Experimental For each accepted followee, i.e. each potential followee the
evaluation showed that the technique improved the precision target user has accepted or manifested interest in, weights are
of static weighting strategies. Results highlighted the import- updated to reflect the new interests of the target user.
ance of adapting to changes in user preferences over time. Ranking Recommended Followees.In standard similarity-
1 Introduction based algorithms, as all recommended candidates are similar
Online social networks have an important place in the life to the target user, they are likely to be similar to each other.
of millions of users who actively use them for finding new Thus, such algorithms will never uncover certain items, which
friends. The decision to start following other users simul- although less similar to the target user, are also important [Hur-
taneously attends to several reasons, which might differ for ley and Zhang, 2011]. Consequently, it would be desirable to
each individual user. Thus, understanding how users select include novel or diverse items in the recommended list. Nov-
followees emerges as a key design factor of strategies to per- elty could be introduced to similarity-based algorithms aiming
sonalise recommendations. Interestingly, most followee selec- at balancing both, the relevance of candidate followees (i.e.
tion approaches are only based on equally important and in- its similarity to the target user) and the diversity of recom-
dependent factors, disregarding how users’ interests can affect mendations. Novelty can be measured in terms of the degree
the followee selection. This work argues that followee recom- to which is unusual regarding the target user normal interests
mendation criteria needs to be personalised according to users’ (i.e. the previously selected followees). It can be computed
abs(Similarity(u,i)−Similarity(u,p f ))
preferences. A technique is proposed for adapting such cri- ∑
as i∈ f ollowees(u) | f ollowees(u)| , where u represents the
teria to each user considering the characteristics of previously target user, p f represents the potential followee, f ollowees(u) rep-
selected followees. resents the previously selected users of u and Similarity is the
2 Related Work overall similarity. If previously selected followees are similar
Several approaches have proposed to suggest interesting to the target user, and the new potential followee is dissimilar
users in social networks based on a unique and independ- to the target user, he/she will also be dissimilar to previously
ent factor [Golder and Yardi, 2010; Hannon et al., 2010]. selected followees. The higher the absolute differences, the
Approaches that combine several factors assume that they higher the dissimilarity, and thus the novelty introduced. Con-
are equally important to each user by averaging or multiply- sequently, the novelty of a potential followee can be assessed
ing them [Armentano et al., 2011]. Closely related to this without computing the actual dissimilarity between the poten-
work, Agarwal and Bharadwaj [2013], and Garcia and Amatri- tial followee and each previously selected followee.
ain [2010] personalised factors’ weights. However, unlike the Finally, the potential followees are ranked by considering
technique proposed by this work, changes over time in user the linear combination of relevance and novelty. The weight
preferences were not considered for adapting the weights. of the novelty is computed as the percentage of the previously
3 An Adaptive Technique for Personalising Fol- selected followees for whom the novelty score was higher than
lowee Recommendation a pre-defined threshold. Similarly, the weight of the relevance
The technique suggests a list of interesting followees by is computed as the percentage of the previously selected fol-
optimally combining different recommendation factors. The lowees for whom novelty was lower than the threshold. Both
combination is particular to each user as it is based on his/her weights are updated as previously described.
preferences reflected on previously selected followees. 4 Experimental Evaluation
Computing Factor Weights.The overall similarity between This section presents the experimental evaluation performed
users u and v (Similarity(u,v)) can be defined as a linear com- to assess the effectiveness of the proposed technique.
bination of the similarity for each followee recommendation Factors for Followee Recommendation. Although the
factor (simi (u,v)) and its corresponding weights (αi ) as follows: presented technique could be applied to any arbitrary number
∑ni=1 αi ∗simi (u,v). As recommendation systems aim to find the of recommending factors, this work focuses in the two main
most similar potential followees, factors’ weights (αi ) should followee recommendation factors: topology and content.
accurately capture user preferences. Thus, they are defined by Topology. Most link prediction algorithms are based on net-
considering the characteristics of the previously selected fol- work topology. The number of common followees is one of the
lowees. Followees are assumed to be chosen by a determined most common metrics applied to Twitter network. It can be
factor if their similarity with the target user for such factor is
higher than a pre-defined threshold. The preference of users defined as |ΓΓout
out (x)∩Γout (y)|
(x)∪Γout (y) , where x and y are nodes (i.e. users),
regarding each factor is computed as the percentage of fol- kx is the degree of node x, and Γ(x), Γout (x) and Γin (x) are the set
lowees for whom the similarity is higher than the threshold. of neighbours, followees and followers of x , respectively.
Figure 1: Comparison of Precision Results
Content. The interest of a user can be characterised Regarding the proposed technique, the adaptive-no-novelty
by profiles based not only on the information they create achieved the worst results. As a result, although the combin-
and publish (publishing profile), but also on the informa- ation of weights is adapted to each user, it is not sufficient
tion they consume (reading profile), for example the retweets. for further improving results. Also, it can be inferred that al-
The publishing profile of user u j is built by considering though users have a particular preference for a certain type of
all of the user tweets (tweets(u j )), which can be defined as: followees, they also select followees that do not exactly match
pub−pro f ile(u j )=tweets(u j ). The reading profile of a user u j can such preferences. Consequently, the search and ranking of
be defined as: read−pro f ileRT (u j )=tweetsRT (uk ) ∀k∈ f ollowees(u j ). The users should not be only guided by similarity, but also by nov-
similarity between the reading profile of a user and the pub- elty. Adding novelty (adaptive) improved the best baseline. As
lishing profile of their potential followees is assessed using the the figure shows, the adaptive alternative was able to achieve
cosine similarity. an optimal precision after 26 weights updates. These results
Experimental Settings.To evaluate the performance of the evidenced the importance of recommending both similar and
proposed technique, potential followees were ranked and the novel followees. Finally, it is also shown the precision stability
top-N users were selected. For each user, their actual fol- once the preferences of users are learned and adapted.
lowees and a equal proportion of randomly selected non- Regarding the differences between the weights predicted by
followees were added to the pool of potential followees to be the technique, and the real preferences of the target users),
recommended. To simulate the actual behaviour of target users the absolute differences were below 0.1 for the 76% of target
over time, actual followees were added to the pool of potential users, highlighting the usefulness of the proposed technique
followees in the same order in which the user started following not only for adequately capturing users’ interests, but also for
them. adapting to the changes in user preferences over time.
The proposed technique (adaptive) was compared against In summary, precision of recommendations can be im-
three static baselines: pure-topology, pure-content and half- proved when considering an adaptive technique for defining
topology-content. Additionally, adaptive was compared to a the weights of the recommendation factors. Results emphas-
version that ignores the novelty factor: adaptive-no-novelty. ised the importance of adapting the relevance or weights of
The quality of recommendations was evaluated by selecting the factors to changes in user preferences over time, and also
a ranked sub-set of the potential followees and computing the considering diversity in followee recommendations.
overall precision immediately after the weights were updated. 5 Conclusions
As there is no explicit feedback from target users available, the This work proposed a technique for adapting the followee
evaluation assumes that items that were not originally part of selection criteria to the decisions of each particular user re-
the followee set are uninteresting for the user. This assumption garding the characteristics of his/her previously selected fol-
might not be completely accurate as recommended users might lowees. Experimental evaluation showed that the proposed
not be selected simply because the user was unaware of them. technique helped to improve precision results regarding static
As a result, precision might be underestimated. weighting strategies. Furthermore, results highlighted the im-
The pool of potential followees comprised 20 users, out of portance of adapting to the changes of the user preferences
which 10 were recommended to the user. Factors’ weights over time.
were updated after 10 accepted recommendations. Initially, References
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1 https://api.twitter.com