Ce diaporama a bien été signalé.
Nous utilisons votre profil LinkedIn et vos données d’activité pour vous proposer des publicités personnalisées et pertinentes. Vous pouvez changer vos préférences de publicités à tout moment.
RecSys 2016 Boston
Modelling Contextual Information in Session-Aware
Recommender Systems with Neural Networks
Bartlomiej T...
1 Problem Definition and Motivation
2 Explicit Session Modeling with Matrix Factorization
3 Automatic Session Modeling with...
Research Motivation
Traditional RS require identified User and persistent Items.
Context-Aware RS (CARS) are not prepared f...
Industry Motivation
Main objective
Capture a user short-term goals as fast as possible.
The 57.06% of all sessions are non...
Implicit Feedback: User-System Interactions
·
source:direct
ua:chrome1
cookie
user-id S1
search-terms:iPhone
S2
sort: pric...
Research Problem - Key Assumptions
User is not identified by known id, just by the current
behaviour.
All users activities ...
User Session Definition
User sessions in this work are defined as uninterrupted sequences of
activity in the system. The ses...
Ephemeral Items and Item Representation
The recommended items are ephemeral i.e. the item life-cycle
is too short or the a...
Item and Event Encoding
All methods presented in this work require items and events being
represented by real-valued vecto...
Matrix Factorization Model
The final estimation is:
ˆy(xs, xi) = (xsQ)(xiP) + xsbp + xibq
where Q ∈ RdS ×d and P ∈ RdI ×d a...
Explicit Session Modeling
The session vector xs aggregates variables from all events within it.
Due to the fact that some ...
Recurrent NN and Feed Forward NN
Both, Recurrent Neural Network (RNN) and Feed Forward Neural
Network (FFNN) are used to p...
NN Architecture
Event EmbeddingItem Embedding
x
(t)
es
xi
RNN Layers
Merge/Dropout Layer
FF Layers
ˆy
(t)
s,i
Figure: Neur...
Datasets
dataset ALLEGRO AVITO
items 24360 4374
sessions 20904 31826
events 535871 767550
searches 366874 110204
density %...
Evaluated Methods
Used methods and its training parameters:
POP
CB: cos
MF: d=100, adagrad
NN: no embedding, single GRU la...
Experiments Results
ALLEGRO AVITO
REC@20 MRR@20 REC@20 MRR@20
NN-BPR-IE 0.4131±.0051 0.1328±.0023 0.1601±.0028 0.0469±.000...
Experiments Results
5 10 15 20
N
0.00
0.05
0.10
0.15
0.20
0.25
0.30
0.35
0.40
0.45
REC@N
Dataset: ALLEGRO
5 10 15 20
N
0.0...
Thank You.
Q&A.
Bartlomiej Twardowski
B.Twardowski@ii.pw.edu.pl, @btwardow
Bartlomiej Twardowski RecSys2016 Boston
References I
Gayo-Avello, D.
A survey on session detection methods in query logs and a proposal for future
evaluation.
Inf...
Prochain SlideShare
Chargement dans…5
×

Recsys 2016: Modeling Contextual Information in Session-Aware Recommender Systems with Neural Networks (Bartłomiej Twardowski)

933 vues

Publié le

Modeling Contextual Information in Session-Aware Recommender Systems with Neural Networks, RecSys 2016 Boston, Bartłomiej Twardowski

Presentation for a paper:
http://dl.acm.org/citation.cfm?id=2959162

Abstract:
Preparing recommendations for unknown users or such that correctly respond to the short-term needs of a particular user is one of the fundamental problems for e-commerce. Most of the common Recommender Systems assume that user identification must be explicit. In this paper a Session-Aware Recommender System approach is presented where no straightforward user information is required. The recommendation process is based only on user activity within a single session, defined as a sequence of events. This information is incorporated in the recommendation process by explicit context modeling with factorization methods and a novel approach with Recurrent Neural Network (RNN). Compared to the session modeling approach, RNN directly models the dependency of user observed sequential behavior throughout its recurrent structure. The evaluation discusses the results based on sessions from real-life system with ephemeral items (identified only by the set of their attributes) for the task of top-n best recommendations.

Publié dans : Sciences
  • Soyez le premier à commenter

Recsys 2016: Modeling Contextual Information in Session-Aware Recommender Systems with Neural Networks (Bartłomiej Twardowski)

  1. 1. RecSys 2016 Boston Modelling Contextual Information in Session-Aware Recommender Systems with Neural Networks Bartlomiej Twardowski Warsaw University of Technology 18 August 2016 Bartlomiej Twardowski RecSys2016 Boston
  2. 2. 1 Problem Definition and Motivation 2 Explicit Session Modeling with Matrix Factorization 3 Automatic Session Modeling with NN 4 Experiments and Evaluation Bartlomiej Twardowski RecSys2016 Boston
  3. 3. Research Motivation Traditional RS require identified User and persistent Items. Context-Aware RS (CARS) are not prepared for handling user sessions data directly. Time in recommender systems is discretized (CARS Tensor Factorization) or used as a bias. Successes of using Neural Networks in other fields: FF/Conv NN in vision and image processing RNN for Natural Language Processing Bartlomiej Twardowski RecSys2016 Boston
  4. 4. Industry Motivation Main objective Capture a user short-term goals as fast as possible. The 57.06% of all sessions are non-logged users and its fingerprint in form of HTTP cookie or device hash does not allow us to identify the user.1 Only 2.53% of all sessions converted to transaction. Most of the sessions are window-shopping ones. The 20.98% of all page views are interactions with search engine. From all sessions, the 35.80% percent used search for finding the right offer. 1 Presented statistics are based on Polish e-marketplace allegro.pl for 3228 M page views sample in January of 2016, where 310 M sessions was identified by HTTP cookie or mobile device hash. Bartlomiej Twardowski RecSys2016 Boston
  5. 5. Implicit Feedback: User-System Interactions · source:direct ua:chrome1 cookie user-id S1 search-terms:iPhone S2 sort: price search-terms:iPhone 5 black V1id1 name2 desc1 seler1 attrib1: value1 . . . V2 V3 S3category:Accessories terms:iPhone 5 black S4 sort: price location: Warsaw C+ 1 add-item:id2 C+ 2 add-item:id3 C− 3 remove-item:id2 V4 V5 B1 item:id3 Figure: Navigational representation of the session for a sample e-marketplace session Bartlomiej Twardowski RecSys2016 Boston
  6. 6. Research Problem - Key Assumptions User is not identified by known id, just by the current behaviour. All users activities in a form of session are available. Items are ephemeral, described by a set of attributes. RS output is Top-N new, recommended items. Bartlomiej Twardowski RecSys2016 Boston
  7. 7. User Session Definition User sessions in this work are defined as uninterrupted sequences of activity in the system. The session ends when the user is inactive for more than a predefined number of minutes [1]. All sessions form a set S = {s1, . . . , sm}, where each session is represented as a set of events ordered in time: sm = {e (1) m , . . . , e (t) m }, where t is the time of the event occurrence in session m. In turn, each event is described by contextual information: e (t) m ∈ CE 1 × CE 2 × · · · × CE k , where number of attributes k depends on collected event e (t) m type. Bartlomiej Twardowski RecSys2016 Boston
  8. 8. Ephemeral Items and Item Representation The recommended items are ephemeral i.e. the item life-cycle is too short or the availability is too dynamic to identify it only by unique id, e.g. news, online auctions. In such settings, known workarounds: Content-Based filtering and items clustering. Assuming that items will never come back - dealing with constant cold-start problem. I = {i1, . . . , in}. Each item is described by a set of defined attributes in ∈ CI 1 × CI 2 × · · · × CI p, where the number of all the item attributes is p. Bartlomiej Twardowski RecSys2016 Boston
  9. 9. Item and Event Encoding All methods presented in this work require items and events being represented by real-valued vectors. Item encoding: CI 1 × CI 2 × · · · × CI p → xi , xi ∈ RdI should exist, where dI is the number of real-values in the encoded item representation. Similarly, the session event CE 1 × CE 2 × · · · × CE k → xe, xe ∈ RdE , where dE is the dimension of the encoded event vector. Bartlomiej Twardowski RecSys2016 Boston
  10. 10. Matrix Factorization Model The final estimation is: ˆy(xs, xi) = (xsQ)(xiP) + xsbp + xibq where Q ∈ RdS ×d and P ∈ RdI ×d are matrices with d-dimensional latent features for session and items variables respectively. Bartlomiej Twardowski RecSys2016 Boston
  11. 11. Explicit Session Modeling The session vector xs aggregates variables from all events within it. Due to the fact that some assumptions have to be made about how all events information should be encoded into single session vector this method is considered as an explicit session modeling. One solution, which is giving good results and is used in this work, is to aggregate event data in a time decaying way xsm = t j=1 1 1+t−j x (j) es,m , where session vector size dS = dE . Bartlomiej Twardowski RecSys2016 Boston
  12. 12. Recurrent NN and Feed Forward NN Both, Recurrent Neural Network (RNN) and Feed Forward Neural Network (FFNN) are used to predict Top-N recommendations for the session. The RNN is used to capture data dependency between session events in time. It uses hidden state as the memory to handle variable length data. In this case, the sequence of events in session. The FFNN is used as a ranking score estimator. It uses the representation of session context returned by RNN and the new items data as an input. Pairwise ranking loss func.: BPR[3], TOP-1[2], WARP[4], k-os WARP. Bartlomiej Twardowski RecSys2016 Boston
  13. 13. NN Architecture Event EmbeddingItem Embedding x (t) es xi RNN Layers Merge/Dropout Layer FF Layers ˆy (t) s,i Figure: Neural Network Layers Architecture. Bartlomiej Twardowski RecSys2016 Boston
  14. 14. Datasets dataset ALLEGRO AVITO items 24360 4374 sessions 20904 31826 events 535871 767550 searches 366874 110204 density % 0.028 0.464 s. len. mean 25.634 24.117 s. len. std 30.282 14.877 Table: Dataset statistics For further processing, event and items in dataset is encoded. Bag-of-words minimal frequency is set to 10. This results in dI =473/2710 and dE =716/5523 and for AVITO/ ALLEGRO datasets respectively. Bartlomiej Twardowski RecSys2016 Boston
  15. 15. Evaluated Methods Used methods and its training parameters: POP CB: cos MF: d=100, adagrad NN: no embedding, single GRU layer with 200, MLP 400, dropout 0.3, rmsprop Bartlomiej Twardowski RecSys2016 Boston
  16. 16. Experiments Results ALLEGRO AVITO REC@20 MRR@20 REC@20 MRR@20 NN-BPR-IE 0.4131±.0051 0.1328±.0023 0.1601±.0028 0.0469±.0009 MF-BPR-IE 0.3849±.0031 0.1150±.0003 0.1894±.0008 0.0404±.0001 NN-TOPK-IE 0.3367±.0057 0.0885±.0020 0.1985±.0031 0.0579±.0004 MF-TOPK-IE 0.2862±.0024 0.0773±.0017 0.2699±.0018 0.0658±.0005 NN-TOPK-I 0.2863±.0048 0.0709±.0025 0.2003±.0012 0.0579±.0004 MF-BPR-I 0.3080±.0013 0.0864±.0012 0.1883±.0015 0.0408±.0000 MF-TOPK-I 0.2353±.0025 0.0586±.0008 0.2691±.0001 0.0660±.0004 CB 0.1858 0.0354 0.1528 0.0243 POP 0.0499 0.0129 0.0193 0.0037 Table: Experiment results for Recall and Mean Reciprocal Rank for Top-20 recommendations. A row label describes used algorithm, loss function and contextual information (I - only items data, IE - items and events context). The mean values with 95% CI are given. Bartlomiej Twardowski RecSys2016 Boston
  17. 17. Experiments Results 5 10 15 20 N 0.00 0.05 0.10 0.15 0.20 0.25 0.30 0.35 0.40 0.45 REC@N Dataset: ALLEGRO 5 10 15 20 N 0.00 0.02 0.04 0.06 0.08 0.10 0.12 0.14 MRR@N Dataset: ALLEGRO NN-BPR-IE MF-BPR-IE NN-TOPK-IE MF-BPR-I NN-TOPK-I CB POP 5 10 15 20 N 0.00 0.05 0.10 0.15 0.20 0.25 0.30 REC@N Dataset: AVITO 5 10 15 20 N 0.00 0.01 0.02 0.03 0.04 0.05 0.06 0.07 MRR@N Dataset: AVITO MF-TOPK-I NN-TOPK-I NN-BPR-IE MF-BPR-IE CB POP Figure: Results in Recall and MRR@Top-N Recommendations. Bartlomiej Twardowski RecSys2016 Boston
  18. 18. Thank You. Q&A. Bartlomiej Twardowski B.Twardowski@ii.pw.edu.pl, @btwardow Bartlomiej Twardowski RecSys2016 Boston
  19. 19. References I Gayo-Avello, D. A survey on session detection methods in query logs and a proposal for future evaluation. Information Sciences 179, 12 (2009). Hidasi, B., Karatzoglou, A., Baltrunas, L., and Tikk, D. Session-based Recommendations with Recurrent Neural Networks. Rendle, S., Freudenthaler, C., Gantner, Z., and Schmidt-thieme, L. BPR : Bayesian Personalized Ranking from Implicit Feedback. In Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence (2009). Weston, J., Bengio, S., and Usunier, N. WSABIE: Scaling up to large vocabulary image annotation. IJCAI International Joint Conference on Artificial Intelligence (2011). Bartlomiej Twardowski RecSys2016 Boston

×