Session-based Recommendation

Definition

Session-based Recommendation

Session-based recommendation predicts the next item(s) a user will interact with based only on the current session’s short-term browsing behavior — the ordered sequence of clicks/views/adds within one visit — rather than a long-lived user profile. It is a paradigm of Sequential Recommendation: items arrive in chronological order and the model conditions on that order.

Canonical example: a user browsing a phone is shown a phone-case; the recommendation is driven by what is happening now, not by who the user is across all time.

Intuition

Why "session" and not "user"

Classic MF / CF treats interactions as an unordered set tied to a stable user identity. That fails in two situations session-based RecSys targets:

• Anonymous / logged-out users — there is no persistent profile, only the events seen so far in this session.
• Short-term intent that overrides long-term taste — a user who usually buys rock music is, right now, building a workout playlist. The session signal is more informative than the historical average.

So we drop the user vector and instead build a representation of the session itself (a short ordered list of items) and decode the next item from it. The split is: Sequential Recommendation = use interaction order; session-based = the special case where the conditioning context is the current session only (often with no user ID), as opposed to user-based recommendation which spans a persistent history.

Mathematical Formulation

Given the current session as an ordered sequence of interacted items $s=⟨i_1,i_2,\dots ,i_t⟩$, the task is next-item prediction: model the conditional distribution over the catalog $I$ for the next position,

$P(i_{t+1}\mid i_1,i_2,\dots ,i_t)$

A simple first-order Markov Chain approximates this with only the last item, $P(i_{t+1}\mid i_t)$, estimated from a global transition matrix. Modern session-based models instead encode the whole prefix into a hidden state. The first deep model, GRU4Rec [Hidasi et al., 2015], uses a GRU RNN that consumes the one-hot items left-to-right:

$h_t=\mathrm{GRU}(h_{t-1},{\mathbf{e}}_{i_t}),{\hat{r}}_s=h_t{W}_{\text{out}}$

where:

• $h_{t-1},h_t$ — recurrent hidden state before/after consuming item $i_t$; $h_t$ summarizes the session so far
• ${\mathbf{e}}_{i_t}$ — dense embedding of the current item (each item has its own learned embedding)
• $W_{\text{out}}$ — output projection producing a score ${\hat{r}}_{s,j}$ for every candidate item $j\in I$
• the highest-scoring items become the recommendation list

GRU4Rec is trained with the pairwise BPR loss (a positive next item vs. sampled negatives):

${\mathcal{L}}_{\text{BPR}}=-\frac{1}{N_S}{\sum }_{j=1}^{N_S}\log \sigma \left({\hat{r}}_{s,i}-{\hat{r}}_{s,j}\right)$

where:

• ${\hat{r}}_{s,i}$ — score for the true next item $i$ in session $s$
• ${\hat{r}}_{s,j}$ — score for negative sample $j$
• $N_S$ — number of negative samples per positive instance
• $\sigma (\cdot )$ — sigmoid; the objective pushes the true next item above sampled negatives

Key Properties / Variants

• Conditioning context: the current session only. Contrast with user-based sequential recommendation, which conditions on a persistent cross-session history. Both are sub-paradigms of Sequential Recommendation.
• No (or optional) user ID: well-suited to anonymous traffic; user/item representations can still be augmented with side-information / content features when available.
• Targets beyond single items: the next-step output can be a single item, a basket, a bundle, or a playlist (next-basket recommendation).
• Model lineage (increasing capacity, all usable as session-based when fed the current session):
  • First-order Markov Chain / Factorized Personalized Markov Chains (FPMC) — short, sparse sessions; first-order transitions only.
  • GRU4Rec (GRU/RNN) — the original deep session-based model; captures short within-session temporal patterns; BPR / TOP1-max loss.
  • Self-Attentive Sequential Recommendation (SASRec) — Self-Attention with item + positional embeddings and a causal mask; faster and stronger than RNN/CNN.
  • BERT4Rec — bidirectional Transformer trained with a Cloze/masked-item objective; appends a [MASK] at the end at inference for next-item prediction.
• Loss matters as much as architecture (Klenitskiy & Vasilev, 2023): SASRec trained with full cross-entropy or BCE with many negatives (“SASRec+”) beats BERT4Rec; too few negatives causes overconfidence. BPR / BCE / CE are model-agnostic.
• Data sparsity is the core difficulty for count-based session models; mitigated by skipping, clustering, and mixture-of-orders, or sidestepped by embedding-based deep models.

Generic next-item inference for an encoder-style session model:

Algorithm: Session-based Next-Item Recommendation (inference)
─────────────────────────────────────────────────────────────
Input: current session s = <i_1, ..., i_t>, item embedding table E
  h ← initial state
  for k = 1 ... t:                 # consume the session in order
    h ← Encoder(h, E[i_k])         # GRU step, or self-attention over prefix
  scores ← h · E^T                 # score ALL candidate items
  mask out items already in s (optional, domain-dependent)
  return Top-K items by score

Connections

• Sub-paradigm of: Sequential Recommendation (uses interaction order); contrast with user-based history
• Departs from: Matrix Factorization / Collaborative Filtering (which ignore order)
• Probabilistic basis: Markov Chain, FPMC
• Deep models: Gated Recurrent Unit (GRU), Recurrent Neural Network (RNN), SASRec, BERT4Rec, Self-Attention, Transformer Model
• Trained with: Bayesian Personalized Ranking (BPR), Negative Sampling
• Output framing: Next-Item Prediction, Top-K Recommendation
• Evaluated with: Recall, MRR, NDCG, Hit Rate (and Beyond-Accuracy Metrics like Diversity)

Appears In

• RS-L01 - Course Overview & Introduction
• RS-L02 - Evaluation Beyond Accuracy
• RS-L03a - Sequential Recommendation Models