태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
The multimodal recommendation task involves developing systems that leverage and integrate multiple types of data—such as text, images, audio, and user interactions—to predict and suggest items that align with a user's preferences. Unlike traditional recommendation approaches that rely on a single data modality, multimodal recommendation harnesses the diverse information from various sources to create richer and more nuanced representations of both users and items. This integration enables the system to understand and capture complex relationships and attributes across different data types, thereby enhancing the accuracy and relevance of the recommendations. The primary goal is to provide personalized suggestions by effectively merging and processing heterogeneous data to better match users with items they are likely to engage with or find valuable.
출처에 같은 태스크 이름으로 연결된 데이터셋입니다. 동명 태스크가 포함될 수 있으므로, 비교할 때는 평가 결과의 조건도 확인하세요.
Amazon Review is a dataset to tackle the task of identifying whether the sentiment of a product review is positive or negative. This dataset includes reviews from four different merchandise categories: Books (B) (2834 samples), DVDs (D) (1199 samples), Electronics (E) (1883 samples), and Kitchen and housewares (K) (1755 samples).
This dataset includes reviews (ratings, text, helpfulness votes), product metadata (descriptions, category information, price, brand, and image features), and links (also viewed/also bought graphs).
This dataset includes reviews (ratings, text, helpfulness votes), product metadata (descriptions, category information, price, brand, and image features), and links (also viewed/also bought graphs).
This dataset includes reviews (ratings, text, helpfulness votes), product metadata (descriptions, category information, price, brand, and image features), and links (also viewed/also bought graphs).
This dataset includes reviews (ratings, text, helpfulness votes), product metadata (descriptions, category information, price, brand, and image features), and links (also viewed/also bought graphs).
This dataset includes reviews (ratings, text, helpfulness votes), product metadata (descriptions, category information, price, brand, and image features), and links (also viewed/also bought graphs).
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.