Random Indexing for Content-based Recommender Systems

IIR 2011 - Italian Information Retrieval Workshop
Milano, Italy

Random Indexing for
Content-based
Recommender Systems
Cataldo Musto - cataldomusto@di.uniba.it
Pasquale Lops, Marco de Gemmis, Giovanni Semeraro

University of Bari “Aldo Moro” (Italy), SWAP Research Group
28.01.11

outline 2/18

• Introduction
• Analysis of Vector Space Models
• Content-based Recommender Systems

• Random Indexing for Content-based Recommender Systems
• Introducing Random Indexing
• Recommendation models

• Experimental Evaluation
• Open Issues
• Future Works

C.Musto, P.Lops, M.de Gemmis, G.Semeraro: Random Indexing for Content-based Recommender Systems - IIR 2011 Workshop - Milano, Italy - 28.01.11

vector space model 3/18

• Weak Points
• High
Dimensionality
• Not incremental
• Does not manage
the latent
semantics of
documents
• Does not manage
negative
preferences


recommender systems 4/18

• A specific type of Information Filtering system that
attempts to recommend information
items (films, television, video on demand, music, books,
etc) that are likely to be of interest to the user

• Content-based Recommender Systems
• The degree of interest is inferred by comparing the
textual features extracted from the item w.r.t. the
features stored in the user profile


goals 5/18

• To investigate the impact of VSM in the
area of content-based recommender
systems
• To introduce techniques able to overcome
VSM typical VSM issues
• Random Indexing
• Dimensionality reduction technique (Sahlgren, 2005)
• Negation Operator
• Based on Quantum Logic (Widdows, 2007)


random indexing 6/18

• Random Indexing (RI) is an incremental and
effective technique for dimensionality reduction
• Introduced by Sahlgren in 2005

• Based on the so-called “Distributional
Hypothesis”
• “Words that occur in the same context tend to
have similar meanings”
• “Meaning is its use” (Wittgenstein)


how it works? 7/18

• Random Indexing reduces
the m-dimensional term/doc
matrix to a new
k-dimensional matrix

• How?
• By multiplying the original matrix
with a random one, built in
an incremental way
• formally: An,m Rm,k = Bn,k
• k << m
• After projection, the distance
between points in the vector space
is preserved

building the matrix 8/18

• A context vector is assignedcan contain only
vector has a ﬁxed dimension (k) and it
for each term. This

values in -1, 0,1. Values are distributed in a random way
but the number of non-zero elements is much smaller.

• The Vector Space representation of a term is obtained
by summing the context vectors of the terms it co-occurs
with.

• The Vector Space representation of a document
(item) is obtained by summing the context vectors of the
terms that occur in it


profile representation 9/18

• What about the user profiles?
• Assumption
• The information coming from documents (items)
that the user liked in the past could be a reliable
source of information for building user profiles

• The Vector Space representation of a user
profile is obtained by combining the context vectors
of all the documents that the user liked in the past.


RI-based approach 10/18

Documents Rating Threshold

VSM representation of RI-based proﬁle for user u

wRI-based approach 11/18

Documents Rating Threshold

Higher weight given to the documents with higher rating


negation operator 12/18

• Both models inherit a classical problem of VSM
• User proﬁles modeled only according to positive
preferences

• In classical text classiﬁers (Naive Bayes, SVM, etc.) both positive and
negative preferences are modeled

• Introduction of a Negation Operator based on
Quantum Logic to tackle this problem
• Query as “A not B” are allowed!
• Projection of vector A on the subspace orthogonal to those generated by the vector B

(*) http://code.google.com/p/semanticvectors/

• Implemented in the Semantic Vectors* open-source package

SV-based approach 13/18

Positive User Profile Vector

Negative User Profile Vector

VSM representation of SV-based profile for user u


wSV-based approach 14/18

Positive User Profile Vector

Negative User Profile Vector

VSM representation of wSV-based profile for user u


recommendation step 15/18

• u and a set of items we can suppose that the most relevant
Given a user proﬁle
items for u are the nearest ones in the vector space
• RI and wRI: Submission of a query based on
• SV and wSV: Submission of a query based on

• Returns the items with as much as possible features from p+ and as
less as possible features from p-

• Cosine Similarity to rank the items
• Items whose similarity is under a certain threshold are labeled as non-relevant
and ﬁltered

• Recommendation of the items with the highest similarity w.r.t.
liked documents are combined.


experimental design 16/18

• Dataset
• Based on MovieLens, enriched with contents
crawled from Wikipedia
• 613 users, 520 items, 25k terms, 40k ratings
• Experiment 1

• Do the weighting schema improve the
predictive accuracy of the recommendation models?
• Experiment 2

• Do the introduction of a negation operator
improve the predictive accuracy of the recommendation
models?


results 17/18
RI W-RI SV W-SV Bayes
Av-Precision@1 85.93 86.33 85.97 86.78 86.39
Av-Precision@3 85.78 85.97 86.19 86.33 85.97
Av-Precision@5 85.75 86.10 85.99 86.16 85.83
Av-Precision@7 85.61 85.92 85.88 85.95 85.77
Av-Precision@10 85.45 85.76 85.76 85.83 85.75

• SV and RI improve the Average Precision with
respect to the Naive Bayes approach (currently
implemented in our recommender system)

17

conclusions 18/18

• Investigation of the impact of Random Indexing in the area of content-based
recommender systems

• Use of Random Indexing for dimensionality reduction
• Introduction of Negation Operator based on Quantum
Logic

• Encouraging experimental results
• First results improve the predictive accuracy
obtained by classical content-based ﬁltering techniques (e.g. Bayes)
• Work-in-progress

• To compare results with classical TF/IDF-based VSM, LSA, Rocchio
and so on


http://www.di.uniba.it/~swap/

discussion

Thanks for your attention

Cataldo Musto - cataldomusto@di.uniba.it

University of Bari (Italy), SWAP Research Group
IIR 2011 - Italian Information Retrieval Workshop

Random Indexing for Content-based Recommender Systems

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