SESSION-AWARE PRODUCT FILTER RANKING IN E- COMMERCE SEARCH

Product filters are commonly used by e-commerce websites to refine search results based on attribute values such as price, brand, size, etc. However, existing filter recommendation approaches typically generate filters independently of the user's search query or browsing history. This can lead to suboptimal recommendations that do not account for what the user has already viewed or selected in their current browsing session. In this paper, we propose a session-aware product filter recommendation framework that leverages user's past actions to provide filter recommendations. An offline evaluation demonstrates that our model achieved significant improvement over non-contextual baseline models.