Multimodal Interest-Shifting Sequence Recommendation with Offline Policy Optimization and Drift-Aware Representation Learning

Interest changes complicate sequential recommendation when interaction histories are combined with item content. We evaluate MM-DRLSR on four public Amazon and Yelp benchmarks. The model integrates category-overlap drift supervision, history-derived drift representations, lightweight identifier–image–text fusion, candidate-conditioned scoring, and an offline actor–critic surrogate objective trained by logged-context replay. The observed next item is used only to construct training labels and rewards; inference ranks candidates from the observed history and candidate content. In the reported five-run summaries under a common leave-one-out protocol, MM-DRLSR attains the highest mean Recall and NDCG among the evaluated methods, with consistent advantages of 0.12–0.22 percentage points over the strongest contemporary multimodal baselines (relative gains of about 1.1–3.6%) that reach Holm-adjusted significance on three of four metrics against Harnessing MLLMs and on Recall@20 against DMESR. The practical value of the method lies in reaching, and in several comparisons, significantly exceeding, the accuracy of heavyweight multimodal-LLM-style approaches with a lightweight architecture whose inference overhead is only about 20% above IDURL. Ablation, sensitivity, and observed interest-shift summaries further describe the contributions of multimodal fusion and offline policy adaptation. The reported results indicate competitive public-data sequential ranking under the stated protocol, together with a reproducible and inference-safe evaluation design.