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IIR 2016, VENEZIA, ITALYCOMPUTING NEIGHBOURHOODS WITH LANGUAGEMODELS IN A COLLABORATIVE FILTERING SCENARIO
Daniel Valcarce, Javier Parapar, Álvaro Barreiro@dvalcarce @jparapar @AlvaroBarreiroG
Information Retrieval Lab@IRLab_UDC
University of A CoruñaSpain
Outline
1. Introduction to Recommender Systems
2. Neighbourhood-based Methods
3. Computing Neighbourhoods
4. Language Models for Neighbourhoods
5. Experiments
6. Conclusions and Future Directions
1/26
INTRODUCTION TO RECOMMENDER SYSTEMS
Recommender Systems
Recommender systems provide personalised suggestions foritems that may be of interest to the users.
Top-N Recommendation: create a ranking of the N mostrelevant items for each user.
Different approaches:
# Content-based: exploit item description to recommenditems similar to those the target user liked in the past.
# Collaborative filtering: rely on the user feedback such asratings or clicks to generate recommendations.
# Hybrid: combination of content-based and collaborativefiltering approaches.
3/26
Recommender Systems
Recommender systems provide personalised suggestions foritems that may be of interest to the users.
Top-N Recommendation: create a ranking of the N mostrelevant items for each user.
Different approaches:
# Content-based: exploit item description to recommenditems similar to those the target user liked in the past.
# Collaborative filtering: rely on the user feedback such asratings or clicks to generate recommendations.
# Hybrid: combination of content-based and collaborativefiltering approaches.
3/26
Collaborative Filtering
Collaborative Filtering (CF) exploit feedback from users:
# Explicit: ratings or reviews.# Implicit: clicks or purchases.
Two main families of CF methods:
# Model-based: learn a model from the data and use it forrecommendation.
# Neighbourhood-based (or memory-based): computerecommendations using directly part of the ratings.
4/26
Collaborative Filtering
Collaborative Filtering (CF) exploit feedback from users:
# Explicit: ratings or reviews.# Implicit: clicks or purchases.
Two main families of CF methods:
# Model-based: learn a model from the data and use it forrecommendation.
# Neighbourhood-based (or memory-based): computerecommendations using directly part of the ratings.
4/26
NEIGHBOURHOOD-BASED METHODS
Neighbourhood-based Methods
Two perspectives:
# User-based: recommend items that users with commoninterests with you liked.
# Item-based: recommend items similar to those you liked.Similarity between items is computed using common usersamong items (not the content!).
6/26
Weighted Sum Recommender (WSR)
Very simple but effective approach (Valcarce et al., ECIR 2016).
WSR computes a weighted sum of the ratings in theneighbourhood. Weights are calculated using cosine similarity.
Item-based version (WSR-IB):
r̂u ,i �∑j∈ Ji
cosine�i , j
�ru , j (1)
User-based version (WSR-UB):
r̂u ,i �∑v∈Vu
cosine (u , v) rv ,i (2)
The computation of neighbourhoods is crucial!
7/26
Weighted Sum Recommender (WSR)
Very simple but effective approach (Valcarce et al., ECIR 2016).
WSR computes a weighted sum of the ratings in theneighbourhood. Weights are calculated using cosine similarity.
Item-based version (WSR-IB):
r̂u ,i �∑j∈ Ji
cosine�i , j
�ru , j (1)
User-based version (WSR-UB):
r̂u ,i �∑v∈Vu
cosine (u , v) rv ,i (2)
The computation of neighbourhoods is crucial!
7/26
COMPUTING NEIGHBOURHOODS
Computing Neighbourhoods with k-NN algorithm
The effectiveness of neighbourhood-based methods relieslargely on how neighbours are computed.
The most common approach is to compute the k nearestneighbours (k-NN algorithm) using a pairwise similarity.
# The most common similarities are Pearson’s correlationcoefficient or cosine similarity.
# Cosine provides important improvements over Pearson’scorrelation coefficient (Cremonesi et al., RecSys 2010).
Let’s study cosine similarity from the perspective ofInformation Retrieval.
9/26
Computing Neighbourhoods with k-NN algorithm
The effectiveness of neighbourhood-based methods relieslargely on how neighbours are computed.
The most common approach is to compute the k nearestneighbours (k-NN algorithm) using a pairwise similarity.
# The most common similarities are Pearson’s correlationcoefficient or cosine similarity.
# Cosine provides important improvements over Pearson’scorrelation coefficient (Cremonesi et al., RecSys 2010).
Let’s study cosine similarity from the perspective ofInformation Retrieval.
9/26
Cosine Similarity and the Vector Space Model
Recommendation Information Retrieval
Target user QueryRest of users Documents
Items Terms
Under this scheme, using cosine similarity for findingneighbours is equivalent to search in the Vector Space Model.
If we swap users and items, we can derive an analogousitem-based approach.
We can use sophisticated search techniques for findingneighbours!
10/26
Cosine Similarity and the Vector Space Model
Recommendation Information Retrieval
Target user QueryRest of users Documents
Items Terms
Under this scheme, using cosine similarity for findingneighbours is equivalent to search in the Vector Space Model.
If we swap users and items, we can derive an analogousitem-based approach.
We can use sophisticated search techniques for findingneighbours!
10/26
Cosine Similarity and the Vector Space Model
Recommendation Information Retrieval
Target user QueryRest of users Documents
Items Terms
Under this scheme, using cosine similarity for findingneighbours is equivalent to search in the Vector Space Model.
If we swap users and items, we can derive an analogousitem-based approach.
We can use sophisticated search techniques for findingneighbours!
10/26
Cosine Similarity and the Vector Space Model
Recommendation Information Retrieval
Target user QueryRest of users Documents
Items Terms
Under this scheme, using cosine similarity for findingneighbours is equivalent to search in the Vector Space Model.
If we swap users and items, we can derive an analogousitem-based approach.
We can use sophisticated search techniques for findingneighbours!
10/26
LANGUAGE MODELS FOR NEIGHBOURHOODS
Language Models
Statistical language models are a state-of-the-art framework fordocument retrieval.
Documents are ranked according to their posterior probabilitygiven the query:
p(d |q) � p(q |d) p(d)p(q)
rank� p(q |d) p(d)
The query likelihood, p(q |d), is based on a unigram model:
p(q |d) �∏t∈q
p(t |d)c(t ,d)
The document prior, p(d), is usually considered uniform.
12/26
Language Models
Statistical language models are a state-of-the-art framework fordocument retrieval.
Documents are ranked according to their posterior probabilitygiven the query:
p(d |q) � p(q |d) p(d)p(q)
rank� p(q |d) p(d)
The query likelihood, p(q |d), is based on a unigram model:
p(q |d) �∏t∈q
p(t |d)c(t ,d)
The document prior, p(d), is usually considered uniform.
12/26
Language Models
Statistical language models are a state-of-the-art framework fordocument retrieval.
Documents are ranked according to their posterior probabilitygiven the query:
p(d |q) � p(q |d) p(d)p(q)
rank� p(q |d) p(d)
The query likelihood, p(q |d), is based on a unigram model:
p(q |d) �∏t∈q
p(t |d)c(t ,d)
The document prior, p(d), is usually considered uniform.
12/26
Language Models for Finding Neighbourhoods (I)
Information Retrieval:
p(d |q) rank� p(d)
∏t∈q
p(t |d)c(t ,d)
User-based collaborative filtering:
p(v |u) rank� p(v)
∏i∈Iu
p(i |v)rv ,i
Item-based collaborative filtering:
p( j |i) rank� p( j)
∏u∈Ui
p(u | j)ru , j
13/26
Language Models for Finding Neighbourhoods (II)
User-based collaborative filtering:
p(v |u) rank� p(v)
∏i∈Iu
p(i |v)rv ,i
We assume a multinomial distribution over the count of ratings.The maximum likelihood estimate (MLE) is:
pmle(i |v) � rv ,i∑j∈Iv rv , j
However it suffers from sparsity. We need smoothing!
14/26
Language Models for Finding Neighbourhoods (II)
User-based collaborative filtering:
p(v |u) rank� p(v)
∏i∈Iu
p(i |v)rv ,i
We assume a multinomial distribution over the count of ratings.The maximum likelihood estimate (MLE) is:
pmle(i |v) � rv ,i∑j∈Iv rv , j
However it suffers from sparsity. We need smoothing!
14/26
Smoothing Methods for Language Models
Absolute Discounting (AD)
pδ(i |u) � max(ru ,i − δ, 0) + δ |Iu | p(i |C)∑j∈Iu ru , j
Jelinek-Mercer (JM)
pλ(i |u) � (1 − λ) ru ,i∑j∈Iu ru , j
+ λ p(i |C)
Dirichlet Priors (DP)
pµ(i |u) � ru ,i + µ p(i |C)µ +∑
j∈Iu ru , j
15/26
EXPERIMENTS
Experimental settings
Baselines:
# Pearson’s correlation coefficient# RM1Sim: user-based similarity (Bellogín et al., RecSys ’13)# Cosine similarity
Our similarities are Language Models using:
# Absolute Discounting smoothing# Jelinek-Mercer smoothing# Dirichlet Priors smoothing
17/26
Parameter Sensibility of WSR-UB on MovieLens 100k
0.18
0 1k 2k 3k 4k 5k 6k 7k 8k 9k 10k
0.280.300.320.340.360.380.40
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
µ
nDC
G@
10
λ, δ
PearsonCosine
RM1Sim (λ)LM-Absolute Discounting (δ)
LM-Jelinek-Mercer (λ)LM-Dirichlet Priors (µ)
18/26
Parameter Sensibility of WSR-IB on R3-Yahoo!
0.0120.0140.0160.0180.0200.0220.0240.0260.0280.030
100 101 102 103 104 105 106
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
nDC
G@
10
µ
λ, δ
PearsonCosine
LM-Absolute Discounting (δ)LM-Jelinek-Mercer (λ)
LM-Dirichlet Priors (µ)
19/26
Precision (nDCG@10)
Algorithm ML 100k ML 1M R3-Yahoo LibraryThing
NNCosNgbr 0.1427 0.1042 0.0138 0.0550PureSVD 0.3595a 0.3499ac 0.0198a 0.2245a
Cosine-WSR 0.3899ab 0.3430a 0.0274ab 0.2476ab
LM-DP-WSR 0.4017abc 0.3585abc 0.0271ab 0.2464ab
LM-JM-WSR 0.4013abc 0.3622abcd 0.0276ab 0.2537abcd
Table: Values of precision in terms of normalised discountedcumulative gain at 10. Statistical significance is superscripted(Wilcoxon two-sided p < 0.01). Pink = best algorithm. Blue = notsignificantly different to the best.
20/26
Diversity (Gini@10)
Algorithm ML 100k ML 1M R3-Yahoo! LibraryThing
Cosine-WSR 0.0549 0.0400 0.0902 0.1025LM-DP-WSR 0.0659 0.0435 0.1557 0.1356LM-JM-WSR 0.0627 0.0435 0.1034 0.1245
Table: Values of the complement of the Gini index at 10.Pink = best algorithm.
21/26
Novelty (MSI@10)
Algorithm ML 100k ML 1M R3-Yahoo! LibraryThing
Cosine-WSR 11.0579 12.4816 21.1968 41.1462LM-DP-WSR 11.5219 12.8040 25.9647 46.4197LM-JM-WSR 11.3921 12.8417 21.7935 43.5986
Table: Values of novelty in terms of Mean Self Information at 10.Pink = best algorithm.
22/26
CONCLUSIONS AND FUTURE DIRECTIONS
Conclusions
Statistical language models are a powerful tool for computingneighbourhoods in a collaborative filtering scenario. Combinedwith WSR, language models:
# Provide highly accurate recommendations.# Improve novelty and diversity figures compared to cosine.# Have low computational complexity.
24/26
Future work
Explore other probability distributions:
# Multivariate Bernoulli.# Multivariate Poisson.
Evaluate the use of inverted indexes to computeneighbourhoods:
# Efficiency.# Scalability.
25/26
THANK YOU!
@DVALCARCEhttp://www.dc.fi.udc.es/~dvalcarce