title: VisualLens: Personalization through Visual History
summary: We hypothesize that a user's visual history with images reflecting their
  daily life, offers valuable insights into their interests and preferences, and
  can be leveraged for personalization. Among the many challenges to achieve this
  goal, the foremost is the diversity and noises in the visual history,
  containing images not necessarily related to a recommendation task, not
  necessarily reflecting the user's interest, or even not necessarily
  preference-relevant. Existing recommendation systems either rely on
  task-specific user interaction logs, such as online shopping history for
  shopping recommendations, or focus on text signals. We propose a novel
  approach, VisualLens, that extracts, filters, and refines image
  representations, and leverages these signals for personalization. We created
  two new benchmarks with task-agnostic visual histories, and show that our
  method improves over state-of-the-art recommendations by 5-10% on Hit@3, and
  improves over GPT-4o by 2-5%. Our approach paves the way for personalized
  recommendations in scenarios where traditional methods fail.
paper_authors._id: 67455f372ec5738026c814d5
paper_authors.name: Wang Bill Zhu
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paper_authors.fullname: Wang Zhu
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paper_authors.user: Bill1235813
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paper_authors.name: Deqing Fu
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paper_authors.fullname: Deqing Fu
paper_authors.type: user
paper_authors.user: deqing
paper_authors._id: 67455f372ec5738026c814d7
paper_authors.name: Kai Sun
paper_authors._id: 67455f372ec5738026c814d8
paper_authors.name: Yi Lu
paper_authors._id: 67455f372ec5738026c814d9
paper_authors.name: Zhaojiang Lin
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paper_authors.fullname: Zhaojiang Lin
paper_authors.type: user
paper_authors.user: zlinao
paper_authors._id: 67455f372ec5738026c814da
paper_authors.name: Seungwhan Moon
paper_authors.status: admin_assigned
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paper_authors.fullname: Seungwhan Moon
paper_authors.type: user
paper_authors.user: shanemoon27
paper_authors._id: 67455f372ec5738026c814db
paper_authors.name: Kanika Narang
paper_authors.status: admin_assigned
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paper_authors._id: 66cd426500cddde87a6404f0
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paper_authors.fullname: Kanika Narang
paper_authors.type: user
paper_authors.user: Kanika91
paper_authors._id: 67455f372ec5738026c814dc
paper_authors.name: Mustafa Canim
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-11-26T22:47:53.221Z
paper_authors._id: 6657cd704018967357c5e135
paper_authors.avatarUrl: /avatars/cfa39b7cf8c00da1a6594c866e3d90df.svg
paper_authors.fullname: Mustafa Canim
paper_authors.type: user
paper_authors.user: mustafacanim
paper_authors._id: 67455f372ec5738026c814dd
paper_authors.name: Yue Liu
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-11-27T22:20:51.000Z
paper_authors._id: 66671b27ad9cd05ea74202a5
paper_authors.avatarUrl: /avatars/a6fa26c173f3e5b0748e5fb007c3105e.svg
paper_authors.fullname: Yue Liu
paper_authors.type: user
paper_authors.user: liu-yue
paper_authors._id: 67455f372ec5738026c814de
paper_authors.name: Anuj Kumar
paper_authors._id: 67455f372ec5738026c814df
paper_authors.name: Xin Luna Dong
paper_title: VisualLens: Personalization through Visual History
paper_summary: We hypothesize that a user's visual history with images reflecting their
  daily life, offers valuable insights into their interests and preferences, and
  can be leveraged for personalization. Among the many challenges to achieve this
  goal, the foremost is the diversity and noises in the visual history,
  containing images not necessarily related to a recommendation task, not
  necessarily reflecting the user's interest, or even not necessarily
  preference-relevant. Existing recommendation systems either rely on
  task-specific user interaction logs, such as online shopping history for
  shopping recommendations, or focus on text signals. We propose a novel
  approach, VisualLens, that extracts, filters, and refines image
  representations, and leverages these signals for personalization. We created
  two new benchmarks with task-agnostic visual histories, and show that our
  method improves over state-of-the-art recommendations by 5-10% on Hit@3, and
  improves over GPT-4o by 2-5%. Our approach paves the way for personalized
  recommendations in scenarios where traditional methods fail.
paper_ai_keywords: VisualLens image representations Hit@3