250310_JH_labseminar[CASER : Personalized Top-N Sequential Recommendation via Convolutional Sequence Embedding].pptx
- 1. CASER : Personalized Top-N Sequential Recommendation via Convolutional Sequence Embedding Ju-Hee SHIM Network Science Lab Dept. of AI The Catholic University of Korea E-mail: [email protected] Jiaxi Tang, Ke Wang WSDM 2018
- 2. 2 INTRODUCTION • Motivation Architecture • CASER • Method Evaluation • Datasets • State-of-the-art methods • Experimental setup • Results CONCLUSION Q/A
- 3. 3 INTRODUCTION Motivation Problems of Existing top-N Recommendation Models • User’s general preferences are used as the basis for recommendations • User’s general preferences : Reflect only static behavioral information of the user (ex. A person who likes Samsung products is only recommended Samsung products, and a person who likes Apple products is only recommended Apple products) • However, these unidirectional models have the following limitations: Simply recommend items related IPHONE : losing the opportunity to recommend phone accessories
- 4. 4 INTRODUCTION Motivation Limitations of Traditional Markov Chain-Based Models a) Point-Level : The probability of purchasing a specific item often increases when multiple past items are combined -> fail to capture this effect (ex. A user who buys milk and butter it likely to purchase flour, but this is not reflected) b) Skip Behaviors : Unable to account for skipped behaviors -> Traditional models assume continuous influence, but in reall-world data, “skip” frequently occur
- 5. 5 Architecture Transforming User Sequences into a Matrix “IMAGE” : • Applying CNN : • Convert the traditional 1D item sequence into an L x d matrix. • L : the most recent L items • d : Embedding dimension • Horizontal Filters : Learning Union-Level Sequential Patterns • Capturing patterns where multiple item combinations influence behavior • Vertical Filters : Learning Point-Level Sequential Patterns • Similar to traditional Markov Chain approaches • Adding User embedding : • Incorporate User Embedding to model Long-term user preferences effectively CASER
- 6. 6 Architecture Transformer Layer: • Consists of L bidirectional Transformer layers. • Each layer refines the user behavior sequence received from the previous layer to enhance representation power. • In each layer, all item representations influence and update each other. • Unlike RNN-based models, which pass information only from past to future, Self-Attention enables global interaction across all items in the sequence. Method
- 7. 7 Architecture Method Embedding Look-up: • Retrieving Past Item Embeddings : • Locate L past item embeddings of user U in the latent space • Stack these embedding to construct the final Embedding matrix (E) for training • Create an embedding table using d-dimensional latent factors • Q(item), P(User)
- 8. 8 Architecture Method Convolutional Layers : • Treat the embedding matrix (E) as an "image" and apply convolutional layers to capture sequential patterns in user behavior • Consider sequential patterns as local features within the image • Utilize two types of convolutional filters: • 1) Vertical Convolutional Layer : • Captures point-level sequential patterns • Computes a weighted sum over the latent representations of the past L items
- 9. 9 Architecture Method Convolutional Layers : • 2) Horizontal Convolutional Layer : • Captures union-level patterns. • Varies the filter height (h) to extract diverse sequential features • To extracted most Significant feature, using max-pooling
- 10. 10 Architecture Method Fully-connected Layers : • Concatenate the outputs from the horizontal and vertical filters • Feed the concatenated features into a fully-connected layer to extract high-level abstract features • Concatenate user embedding with the extracted features to capture general user preferences -> Pass the final representation to the output layer for prediction
- 11. 11 Architecture Method Network Training & Recommendation: • Apply the sigmoid activation function to the output layer to transform the output value y into a probability. • Compute the likelihood across all sequences in the dataset for training • Use the user’s last L item embeddings to compute y-values for all items • Select the top-N items with the highest y-values for recommendatio
- 12. 12 Datasets Evaluation MovieLens Gowalla Foursquare Tmall
- 13. 13 Evaluation State-of-the-art methods Compared Methods • POP • BPR • FMC • FPMC • Fossil • GRU4Rec
- 14. 14 Evaluation Experimental setup •Evaluation Metrics • Precision@N • Recall@N • MAP •Optimizer: Adam •Learning Rate: {1,10^-1,…,10^-4} grid search •Batch Size : 100 •L2 Regularization •Dropout : 50% •Latent Dimensions d : {5,10,20,30,50,100} •Markov Order L : {1,2,3,…,9} •Target Number T : {1,2,3} •Activation Functions : {identity, sigmoid, tanh, ReLU} •Number of Horizontal Filters : {4,8,16,32,64} •Number of Vertical Filters : {1,2,4,8,16} •Loss : BCE loss •Negative Sampling : random item 3
- 16. 16 Evaluation Results Ablation Study Results • Caser model outperforms Fossil, GRU4Rec in terms of MAP, with the best performance observed at T =2,3. • As the Markov Order L increases, performance improves and then plateaus; in sparse datasets, excessively large L can lead to performance degradation. • Markov Targets T contributes to performance improvement = Predicting multiple future items simultaneously is more effective than predicting just one
- 17. 17 Evaluation Results Ablation Study Results • Performance results based on the usage of each compontent • P : personalization(user embedding), h: horizontal convolutional layer, v : vertical convolutional layer • The best performance is achieved when all three components are used together
- 18. 18 Conclusion Conclusion The author of this paper was proposing CASER, a novel approach to top-N sequential recommendation. CASER captures information from point-level and union-level sequential patterns, skip behaviors, and long- term user preferences. A unique aspect of CASER is it’s attempt to interpret a user’s 1D sequence as a 2D image representation. This approach could be particularly meaningful in industries where the sequential dependency of user behavior is weak.
- 19. 19 Q & A Q / A
Editor's Notes
- #19: thank you, the presentation is concluded