Multi-modal User Interactions for Recommendations

<p>This repository contains the data for <a href="https://doi.org/10.1145/3626772.3657881">Dataset and Models for Item Recommendation Using Multi-Modal User Interactions</a>.</p> <p>We publish a real-world dataset from the insurance domain with multi-modal user interactions that can be used in recommendation models. The dataset is anonymized.</p> <p>There are 6 different datasets:</p> <div> <h3><strong>data_users.csv</strong></h3> </div> <p>This data contains the users. Each user has had one or more purchase events with conversations and/or web sessions prior to that purchase. The data contains 5 columns:</p> <ul> <li>user_id. The ID of a user.</li> <li>purchase_event_id. The ID of a purchase event.</li> <li>conversation_id. The ID of a conversation.</li> <li>session_id. The ID of a web session.</li> <li>event_number. A number specifying the order of conversations/web sessions.</li> </ul> <div> <h3><strong>data_conversations_keyword.csv</strong></h3> </div> <p>This data contains the conversations that the user had prior to the user's purchase event. Each conversation consists of multiple sentences represented with keywords. The data contains 4 columns:</p> <ul> <li>conversation_id. The ID of a conversation.</li> <li>sentence_number. A number specifying the order of sentences.</li> <li>sentence_speaker. The speaker of the sentence (user or agent).</li> <li>keywords. List with the IDs of the keywords in the sentence.</li> </ul> <div> <h3><strong>data_conversations_embedding.csv</strong></h3> </div> <p>The data contains the conversations that the user had prior to the user's purchase event. Each conversation consists of multiple sentences represented with text embeddings. The data contains 771 columns:</p> <ul> <li>conversation_id. The ID of a conversation.</li> <li>sentence_number. A number specifying the order of sentences.</li> <li>sentence_speaker. The speaker of the sentence (user or agent).</li> <li>embedding_1 - embedding_768. Text embeddings computed with a pre-trained language-specific BERT model.</li> </ul> <div> <h3><strong>data_sessions.csv</strong></h3> </div> <p>This data contains the web sessions that the user made prior to the user's purchase event. Each web session consists of multiple actions. The data contains 3 columns:</p> <ul> <li>session_id. The ID of a web session.</li> <li>action_number. A number specifying the order of actions.</li> <li>action_tags. List with the IDs of the section, object and type of an action.</li> </ul> <div> <h3><strong>data_purchase_events.csv</strong></h3> </div> <p>This data contains the purchase events. Each event consists of one or more item purchases made by the same user. The data contains 2 columns:</p> <ul> <li>purchase_event_id. The ID of a purchase event.</li> <li>item_id. The ID of an item.</li> </ul> <div> <h3><strong>data_post_filter.csv</strong></h3> </div> <p>This data contains the items that were possible for the user to buy at the time of the user's purchase event. The data contains 2 columns:</p> <ul> <li>purchase_event_id. The ID of a purchase event.</li> <li>item_id. The ID of an item.</li> </ul>