# Translation-based Recommendation

arXiv: https://arxiv.org/abs/1707.02410 · HTML: https://mlsanity.com/paper/1707.02410/

## Key facts

- Cited by 71 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Jul 2017.

## Built on by (most cited first, 30 of 71)

- [Large Language Models are Zero-Shot Rankers for Recommender Systems](https://mlsanity.com/paper/2305.08845/) (2023)
- [A Survey on Accuracy-oriented Neural Recommendation: From Collaborative Filtering to Information-rich Recommendation](https://mlsanity.com/paper/2104.13030/) (2021)
- [Usable XAI: 10 Strategies Towards Exploiting Explainability in the LLM Era](https://mlsanity.com/paper/2403.08946/) (2024)
- [Sequential Recommendation via Stochastic Self-Attention](https://mlsanity.com/paper/2201.06035/) (2022)
- [Turning Dross Into Gold Loss: is BERT4Rec really better than SASRec?](https://mlsanity.com/paper/2309.07602/) (2023)
- [Online certification of preference-based fairness for personalized recommender systems](https://mlsanity.com/paper/2104.14527/) (2021)
- [Modeling Sequences as Distributions with Uncertainty for Sequential Recommendation](https://mlsanity.com/paper/2106.06165/) (2021)
- [Lightweight Self-Attentive Sequential Recommendation](https://mlsanity.com/paper/2108.11333/) (2021)
- [Image Fusion for Cross-Domain Sequential Recommendation](https://mlsanity.com/paper/2502.15694/) (2024)
- [RecGURU: Adversarial Learning of Generalized User Representations for Cross-Domain Recommendation](https://mlsanity.com/paper/2111.10093/) (2021)
- [Learning Compressed Embeddings for On-Device Inference](https://mlsanity.com/paper/2203.10135/) (2022)
- [Pacer and Runner: Cooperative Learning Framework between Single- and Cross-Domain Sequential Recommendation](https://mlsanity.com/paper/2407.11245/) (2024)
- [Time to Split: Exploring Data Splitting Strategies for Offline Evaluation of Sequential Recommenders](https://mlsanity.com/paper/2507.16289/) (2025)
- [Probabilistic Metric Learning with Adaptive Margin for Top-K Recommendation](https://mlsanity.com/paper/2101.04849/) (2021)
- [LLM-Enhanced Multimodal Fusion for Cross-Domain Sequential Recommendation](https://mlsanity.com/paper/2506.17966/) (2025)
- [Towards Out-of-Distribution Sequential Event Prediction: A Causal Treatment](https://mlsanity.com/paper/2210.13005/) (2022)
- [Learning to Limit Data Collection via Scaling Laws: A Computational Interpretation for the Legal Principle of Data Minimization](https://mlsanity.com/paper/2107.08096/) (2021)
- [Recommender systems based on graph embedding techniques: A comprehensive review](https://mlsanity.com/paper/2109.09587/) (2021)
- [Towards Fair Recommendation in Two-Sided Platforms](https://mlsanity.com/paper/2201.01180/) (2021)
- [Learning Self-Modulating Attention in Continuous Time Space with Applications to Sequential Recommendation](https://mlsanity.com/paper/2204.06517/) (2022)
- [Accurate Action Recommendation for Smart Home via Two-Level Encoders and Commonsense Knowledge](https://mlsanity.com/paper/2208.06089/) (2022)
- [Position-enhanced and Time-aware Graph Convolutional Network for Sequential Recommendations](https://mlsanity.com/paper/2107.05235/) (2021)
- [Sequential Modeling with Multiple Attributes for Watchlist Recommendation in E-Commerce](https://mlsanity.com/paper/2110.11072/) (2021)
- [Determinantal Point Process Likelihoods for Sequential Recommendation](https://mlsanity.com/paper/2204.11562/) (2022)
- [Scaling Sequential Recommendation Models with Transformers](https://mlsanity.com/paper/2412.07585/) (2024)
- [Learning Enhanced Representations for Tabular Data via Neighborhood Propagation](https://mlsanity.com/paper/2206.06587/) (2022)
- [Cooperative Retriever and Ranker in Deep Recommenders](https://mlsanity.com/paper/2206.14649/) (2022)
- [Just Ask for Music (JAM): Multimodal and Personalized Natural Language Music Recommendation](https://mlsanity.com/paper/2507.15826/) (2025)
- [Explore User Neighborhood for Real-time E-commerce Recommendation](https://mlsanity.com/paper/2103.00442/) (2021)
- [TEA: A Sequential Recommendation Framework via Temporally Evolving Aggregations](https://mlsanity.com/paper/2111.07378/) (2021)

## Abstract (arXiv)

Modeling the complex interactions between users and items as well as amongst items themselves is at the core of designing successful recommender systems. One classical setting is predicting users' personalized sequential behavior (or `next-item' recommendation), where the challenges mainly lie in modeling `third-order' interactions between a user, her previously visited item(s), and the next item to consume. Existing methods typically decompose these higher-order interactions into a combination of pairwise relationships, by way of which user preferences (user-item interactions) and sequential patterns (item-item interactions) are captured by separate components. In this paper, we propose a unified method, TransRec, to model such third-order relationships for large-scale sequential prediction. Methodologically, we embed items into a `transition space' where users are modeled as translation vectors operating on item sequences. Empirically, this approach outperforms the state-of-the-art on a wide spectrum of real-world datasets. Data and code are available at https://sites.google.com/a/eng.ucsd.edu/ruining-he/.