Algorithmic Explanation - Advanced Recommendations

Algorithmic Details

Each of these models has a different algorithm to learn and generate recommendations.

Recommended Items (Similar Users) Hierarchical Recurrent Neural Network (HRNN)
Similar Items

Item-item Collaborative filtering +

Item attribute Similarity

Frequently Bought Together Item-item Collaborative filtering (Purchase events)
Frequently Viewed Together Item-item Collaborative filtering (View events)

 

Hierarchical Recurrent Neural Network (HRNN)

HRNN identifies patterns in how users interact with different items on your platform. By doing so, it can predict which item a user is likely to be interested in next, based on their past behavior. This approach assumes that if user A has the same opinion as user B (where B is a similar user in terms of interaction patterns) in a session, A is more likely to have B's opinion on a different session than that of a random user.

  1. User Behavior Patterns: The key to HRNN's effectiveness is its ability to deduce relationships between items based on their interaction patterns, even if the items don't have explicit attributes linking them. These patterns might include which items are viewed together or in close sequence.
  2. Embedding Layers: In HRNN, both users and items are represented by embeddings, which are dense vectors of floating-point values. The model uses these embeddings to represent the 'closeness' of items and users in a high-dimensional space. Items that are frequently interacted with by users are positioned closer to each other in this space.
  3. Recurrent Layers: The HRNN uses recurrent layers to remember patterns across long sequences of data, allowing it to make better predictions and identify complex relationships between items. The timestamp of the interaction event gives the temporal edge to recommendations beyond sequences.

Collaborative Filtering

Item-item collaborative filtering is a specific approach focusing on the relationships between items rather than between users. Item-item collaborative filtering tends to be more stable (but less personalized) than Similar User behavior models because item preferences change less frequently than user preferences.

This approach examines the co-interactions of users with items and then determines the similarity of items at a global level based on interaction data. To give an idea, refer to the following basic interpretation of how the computation is done.

User ID Item ID Timestamp
U1 P1 2023-07-23 11:24:02.112
U1 P2 2023-07-24 12:20:05.096
U1 P3 2023-08-12 04:34:34.234
U2 P1 2023-07-05 12:30:00.090
U2 P2 2023-08-02 14:45:45.010
U3 P1 2023-08-25 15:52.12.020
U4 P4 2023-08-24 05:52.12.020
U4 P1 2023-06-24 03:25.12.020
U4 P2 2023-07-09 06:37.25.100
U4 P5 2023-08-04 09:35.14.080
U4 P1 2023-06-24 01:25.12.456
U5 P2 2023-07-14 04:15.22.030
U5 P4 2023-08-04 10:25.12.120

This can be transformed to an item-item matrix based on no of users who interacted with the items, as following

  P1 P2 P3 P4 P5
P1 5 4 1 2 1
P2 4 4 1 2 1
P3 1 1 1 0 0
P4 2 2 0 2 1
P5 1 1 0 1 1

Hence, excluding the relationship of the item with itself, the recommendation results will be as follows:

Anchor item (last interacted item) Results from Collaborative filtering
P1 P2, P4, P3, P5
P2 P1, P4, P3, P5
P3 P1, P2
P4 P1, P2, P5
P5 P1, P2, P4

Note: the above explanation is merely a basic representation of logic. The actual algorithm is much more complicated than this.

Item Attributes Similarity

To capture the relationships among items, the item metadata embedding is created for each item ID that is essentially represented in multi-dimensional space, where each dimension corresponds to a hidden variable or latent factor. These embeddings represent the attributes of the items, such as genre or category.

After generating item embeddings from user-item interactions and item metadata, they're merged in a fusion layer. This layer, during training, learns to adjust weights based on item characteristics and user preferences. It decides the importance of embeddings from user interactions versus those from item metadata. Through a deep learning model, it effectively balances recommendations by weighing both interaction data and item metadata similarities.

Popularity Count

Popularity count is a weighted aggregation-based model, but it suggests the most popular items based on overall interaction counts. This plays a role of fallback for scenarios where historical data is minimal or unavailable.

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