Post on 07-Apr-2019
transcript
Cold Star t So lu t ions fo r Recommender Sys tems Amin Mantrach
amantrac@yahoo-inc.com Research Scientist – Yahoo Labs Barcelona
Outline
§ Recommending at cold start; › Learning representations for the item cold start:
• Recommending cold articles to users;
› Enrich user profiles by using users’ implicit feedback: • Learning representations for completing the user profile;
› Enrich user profiles by using query logs; § Discussions: Matrix factorization and skip gram.
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RECOMMENDING AT COLD START
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Item Cold Start Problem on Yahoo Properties
§ Majority of the items (~80%) are never shown or clicked:
§ Personalization uses content as main signal (CTR can not be used on cold items);
§ Motivations: why recommending cold start items? › Diversify the offer; › Avoid the “kim kardashian” effect; › Avoid quick sold out of advertisings.
Weakly-engaged users on Yahoo Properties
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§ User engagement is power-law distributed; à ~80% of the users have sparse profiles: on Netflix, Amazon, Yahoo news and Yahoo search. § In other words, we are facing a coverage problem; § Recommendations can not be efficient for the majority of the users due
to the sparsity of their profiles.
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Drawbacks of state-of-the-art in cold-start recommendations Item cold start § State-of-the-art of collaborative filtering approaches can not be applied:
[2009 Koren et al., Matrix factorization techniques for recommender systems; 2009 Rendle et al. UAI, B.P. Ranking from implicit feedback];
§ Basic approaches relies on content-based (CB) models. § State-of-the-art for item cold-start consists of hybrid methods
[WSDM 2010, Agarwal et al., fLDA; Gantner ICDM 2010, learning mappings…];
Weakly-engaged user § 80% of the users are weakly-engaged and thus have sparse profiles; § State-of-the art user profile enrichment techniques rely on
› the kNN to enrich the user profile (this does not work for the weakly-engaged user); › external information but low coverage.
Our contributions to the cold start
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§ Novel collective learning representation framework: › Common framework:
• We solve both the item cold start and the user cold start; • Our representations are interpretable (non-negative) and can be used to
reconstruct the user profile; • Our implementation relies on simple alternating least squares (ALS) or
multiplicative updates (MU).
§ Weakly-engaged users: • We can complete better the user profiles in comparison to the state-of-the-
art.
The cold start: Research Questions
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Content + User feedback à Collective factorization Singh and Gordon KDD 2008
Item cold start: 1.Can we learn collective representations from content and
collaborative information to outperform state-of-the-art item cold-start recommenders? [Saveski and Mantrach – RecSys 2014]. Weakly-engaged user:
2.Can we design collective representations for enriching the weakly-engaged user profile using implicit user feedback? [ongoing work]
3.Can we use query logs as external information to improve recommendations on homerun ? [patent filed]
The cold start: Research Questions
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Content + User feedback + Matrix factorization à Collective factorization Singh and Gordon KDD 2008
Item cold start: 1.Can we learn collective representations from content and
collaborative information to outperform state-of-the-art item cold-start recommenders? [Saveski and Mantrach – RecSys 2014]. Weakly-engaged user:
2.Can we design collective representations for enriching the weakly-engaged user profile using internal user feedback?
3.Can we use external informational – as query logs – to improve recommendations ? [patent filed]
1. RECOMMEND FOR THE ITEM COLD START
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Collective Representation Learning
§ Why collective? › It allows learning from multiple sources: users’ feedback + items’ features.
§ Why representation? › By learning embeddings we extract latent factors that capture the essence of the data.
§ Why collective representation for cold start? › When observing just one view we can reconstruct the missing one .
By projecting items’ features on the joined representation we can
reconstruct missing the user’s items
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Xs W Hs
≈ Global
topic model TOPICS
Topic 1 …
Xu W Hu
≈ C1 …Ck
Topic k
#features
#users
#ite
ms
#ite
ms
COMMUNITY Personalized
model per user
Collective Representation Learning
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Non-negative representations + locality constraints: LCE 2 similar items should share similar representations
Optimization Problem
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§ We implemented an alternating least squares algorithm and a multiplicative update algorithm to learn the decomposition.
[https://github.com/amantrac/JNMF]
Item Cold-Start Recommendations
Offline evaluation: § Enron: 10 mailboxes, 36K emails, 5K users, explicit feedback. § Yahoo News Articles (40days – random sample of 41K articles –
650K users + user implicit feedback (3.5M comments).
A/B testing › Average #of items surfaced/day; › Dwell time of the items
[RecSys2014, Yi et al., Beyond clicks: dwell time for personalization.]
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Item Cold Start: Baselines
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1. Content Based Recommender (CB) 2. Content Topic Based Recommender
3. Latent Semantic Indexing on user profiles [Soboroff’99]
4. Author Topic Model [M. Rosen-Zvi’04]
5. Bayesian Personalized Ranking + kNN (BRP-kNN) [Gantner’10]
6. fLDA [Agarwal’10]
Offline Evaluation: Email Recipients Recommendation
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MicroF1 MacroF1 MAP NDCG
Performan
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BPR-kNN CB LCE (No GR) LCE
Offline Evaluation: Cold News Articles Recommendation
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CB BPR-kNN LCE (No GR) LCE
Next directions…
§ Use dwell time/duration (i.e. proportion of the video watched) instead of intentional plays;
§ Incorporate a profile enrichment strategy based on representation learning to diversify recommendations for the weakly-engaged user.
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The cold start: Research Questions
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Content + User feedback + Matrix factorization à Collective factorization Singh and Gordon KDD 2008
Item cold start: 1.Can we learn collective representation from content and
collaborative information to outperform state-of-the-art item cold-start recommenders? [Saveski and Mantrach – RecSys 2014]. Weakly-engaged user:
2.Can we design collective representations for enriching the weakly-engaged user profile using internal user feedback?
3.Can we use external informational – as query logs – to improve recommendations ? [1 patent – submitted to Techpulse 2014]
Recommending in the Long Tail: User Profile Completion Why it is important: § Current rec. systems are only effective for the 20% loyal users having
a dense profile. Why enriching weakly engaged user ? § Improving recommendation for 80% of the remaining users; § Encouraging churning of weakly-engaged users to loyal; § Easy to integrate: we feed the existing system with enriched user
profiles and do not need to change existing algorithms; § Advertising can benefit as well of better enriched profiles.
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Endogenous vs Exogenous Profile Enrichment
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A. Endogenous: Using implicit feedback § We have this info for loyal users for free; § We do not need to rely on any external source. Our solution: § Learning embedding spaces designed to reconstruct user profiles to
improve news recommendation.
B. Exogenous § Many external sources of information are available inside Yahoo. They can
be used to enrich user profiles. § Our solution: § Using search query logs to enrich user profiles for news recommendation.
The cold start: Research Questions
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Content + User feedback + Matrix factorization à Collective factorization Singh and Gordon KDD 2008
Item cold start: 1.Can we learn collective representation from content and
collaborative information to outperform state-of-the-art item cold-start recommenders? [Saveski and Mantrach – RecSys 2014]. Weakly-engaged user:
2.Can we design collective representations for enriching the weakly-engaged user profile using implicit user feedback?
3.Can we use external informational – as query logs – to improve recommendations ? [1 patent – submitted to Techpulse 2014]
2. USING IMPLICIT USER FEEDBACK 23
User Coverage Against Click Count for News Data Set
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Xs W Hs
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Xu Hu
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Collective Representation Learning for User Profiles Reconstruction
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Xp ≈ Hu Hs
#use
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User Profile Reconstruction Xp=XuT.Xs
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Optimization Problem
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User Profile Reconstruction Regularization
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NDCG
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clicks=1clicks=2clicks=3clicks=4clicks=5
Performance in Terms of Sparsity
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NDCG
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CEUP-ACLS
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WITHOUT ENRICHMENT
The cold start: Research Questions
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Content + User Feedback + Matrix Factorization à Collective Factorization Singh and Gordon KDD 2008
Item cold start: 1.Can we learn collective representation from content and
collaborative information to outperform state-of-the-art item cold-start recommenders? [Saveski and Mantrach – RecSys 2014]. Weakly-engaged user:
2.Can we design collective representations for enriching the weakly-engaged user profile using internal user feedback? [submitted to techpulse 2014]
3.Can query logs be used as external information to improve recommendationso ? [1 patent]
B. USING EXTERNAL SIGNAL QUERY LOGS 30
News Personalization
§ Reading profile (endogenous): Aggregated clicked news (implicit feedback) or skipped news.
§ Search profiles (exogenous): Aggregated queries submitted by users (explicit feedback).
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Search profiles
§ Motivations: using other sources of available information to improve news personalization.
§ Why search? › More familiar; › Explicit user intent.
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Query
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Titles
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Abstracts
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Coverage
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66% of the Homerun users in a specific target day did also use “Search” during the last month.
Others Finance FrontPage Mail News Search SportsYahoo! properties
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Coverage
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§ Considering users who clicked at least once on a Homerun recommendation on a target day, how many queries did each of them submit during the last 3 months?
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Number of queries per user during 90 days
§ Users that clicked at least once during a target day on a recommended article;
§ We consider only users who submitted at least 1000 queries during the last 3 months (~10 query/day);
à 70K users with 140K recommendations.
Data Set
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User Query
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User QTitles
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User QAbstracts
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User Query 1
User Query 2
User Query 3
User Query N
Query User Profile
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User QTitles 1
User QTitles 2
User QTitles 3
User QTitles N
QTitle User Profile
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GR
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User QAbstracts 1
User QAbstracts 2
User QAbstracts 3
User QAbstracts N
QAbstracts User Profile
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I. Do search profiles help improve the quality of news personalization?
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I. Do search profiles help improve the quality of news personalization?
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II. What are the important features to be considered in a search profile?
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III. How many queries do we need?
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Limitation: 400 queries corresponds to a coverage of ~200K users
IV. Which period should the historical search information span in order to produce high-quality recommendations?
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V. How does the recency of search profiles affect the quality of news personalization?
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Status and Limitations
§ The main limitation is the coverage: › Scales up to 200K users.
§ Further work: › Improve coverage; › Complete user profile by learning collective representations from (1) implicit
feedback, (2) query logs and (3) item’s features.
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Discussions: Matrix Factorizations and skip-gram based Representations
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§ Tomas Mikolov, Kai Chen, Greg Corrado, and Jeffrey Dean. Efficient Estimation of Word Representations in Vector Space. In Proceedings of Workshop at ICLR, 2013
§ “We analyze skip-gram with negative-sampling (SGNS), a word
embedding method introduced by Mikolov et al., and show that it is implicitly factorizing a word-context matrix, whose cells are the pointwise mutual information (PMI) of the respective word and context pairs (shifted by a global constant).” [Omer Levy and Yoav Goldberg,Neural Word Embeddings as Implicit Matrix Factorization, NIPS, 2014]
Quest ions Doubts
Concerns Queries
Quest ion Advises Issues
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