目的

推薦システムに関する論文は大量にありますが、企業の開発者が実際に気にすることとしては「それ使えるの？」ということでしょう。もちろん、あるアプリケーションで適用できるからといって、他のアプリケーションに適用できるとは限りませんが、実例は大変参考になります。

実例

Youtube

Google Play

Wide & Deep Learning for Recommender Systems

Pinterest

An update on Pixie, Pinterest’s recommendation system
- グラフ構造を作成し、そこでランダムウォークを実施して、最も訪問回数を多いものをレコメンドしていく
- Pinterest object graph (the graph between Pins and boards)
  - how those Pins are organized based on the context people add as they save and the Pinner’s interests.
- Pixie then finds the Pins most relevant to the user by applying a random walk algorithm for 100,000 steps.
  - At each step, it selects a random neighbor and visits the node, incrementing node visit counts as it visits more random neighbors.
  - We also have a probability Alpha, set at 0.5, to restart at node Q so our walks do not stray too far.
  - We continue randomly sampling the neighboring boards and nodes for 100,000 steps.
  - Once the random walks are complete, we know the nodes which have been visited most frequently are the ones most closely related to the query node.
- Optimizing Pixie
  - Early Stopping
    - we keep walking until the rank 1,000 candidate gets at least 20 visits.
  - Graph Prunning
    - we can remove some of those edges to make Pixie suit our needs

Amazon

Two Decades of Recommender Systems at Amazon.com
- 色々と工夫が必要となりそうだが、item-based collaborative filteringは強力であるとのこと
- 途中 E_xyやN_xyが出てくるが、これは類似度計算を計算するために使っている。最終的にアイテム間の類似度行列を使ってアイテムのスコアを出すのがitem-based collaborative filteringということなのだろうか（類似度行列の計算方法は規定されない）。
- We found that, given enough data and a robust metric for the relatedness of items,
  ompatibility can emerge from people’s behavior, with the false signals failing away and the truly appropriate items surfacing.
- Relatedness scores need to strike a balance between popularity on one end and the power law distribution of unpopular items on the other. The chi-square score, [ N_XY − E_XY] / √E_XY , is an example that strikes such a balance.
- (the item-based collaborative filtering) (略) has been adapted to improve diversity and discovery, recency, time-sensitive or sequential items, and many other problems.
- Because of its simplicity, scalability, explainability, adaptability, and relatively high-quality recommendations, item-based collaborative filtering remains one of the most popular recommendation algorithms today.
Hierarchical Temporal-Contextual Recommenders

Instagram

Powered by AI: Instagram’s Explore recommender system
- word2vecを活用したアカウントのembeddingとuserのアクティビティ（いいね、保存etc）からuserを大雑把にまず絞り込む。絞り込まれたuserの投稿について、蒸留を用いたモデルを含めた3つのランキングモデルを活用し、[w_like * P(Like) + w_save * P(Save) - w_negative_action * P(Negative Action)]の値に基づいてランキングする。
- Account embeddings for personalized ranking inventory
  - As a result, content-based models have difficulty grasping such a variety of interest-based communities.
  - By applying the same techniques from word2vec, we can predict accounts with which a person is likely to interact in a given session within the Instagram app. If an individual interacts with a sequence of accounts in the same session, it’s more likely to be topically coherent compared with a random sequence of accounts from the diverse range of Instagram accounts.
  - Retrieving accounts that are similar to those that a particular person previously expressed interest in helps us narrow down to a smaller, personalized ranking inventory for each person in a simple yet effective way.
- Preselecting relevant candidates by using model distillation
  - 蒸留したモデルを使って、より複雑なモデルで評価するmediaを絞り込む
- How we built Explore
  - Exploreをthe candidate generation stageとthe ranking stageに分ける
- Candidate Generation
  - Then, we use account embeddings techniques to identify accounts similar to the seed accounts. Finally, based on these accounts, we’re able to find the media that these accounts posted or engaged with.
  - In addition to blocking likely policy-violating content and misinformation, we leverage ML systems that help detect and filter content like spam.
  - Then, for every ranking request, we identify thousands of eligible media for an average person, sample 500 candidates from the eligible inventory, and then send the candidates downstream to the ranking stage.
- Ranking candidates
  1. First pass: the distillation model mimics the combination of the other two stages, with minimal features; picks the 150 highest-quality and most relevant candidates out of 500.
  2. Second pass: a lightweight neural network model with full set of dense features; picks the 50 highest-quality and most relevant candidates.
  3. Final pass: a deep neural network model with full set of dense and sparse features. Picks the 25 highest-quality and most relevant candidates (for the first page of Explore grid).
  - We downrank posts from the same author or same seed account by adding a penalty factor, so you don’t see multiple posts from the same person or the same seed account in Explore.
  - We rank the most relevant content based on the final value model score of each ranking candidate in a descendant way.

Netflix

Spotify

Music recommendation at Spotify
- Areas of research expertise
  - Machine learning
  - Information retrieval
  - Evaluation
  - Language technologies
  - Content analysis
  - Algorithmic bias
  - Human computer interaction
  - Recommender systems
  - User modeling
Explore, Exploit, and Explain: Personalizing Explainable Recommendations with Bandits

Gunosy

Gunosyのニュース推薦システムの仕組みと、それを支えるAWSアーキテクチャの裏側

Wantedly

Wantedly Visitの機械学習を使ったレコメンデーション

Mercari

ナレッジグラフを使った解釈可能な推薦システム