Totsvector

Totsvector is a term used in some technical discussions to describe a compact, fixed-length vector representation that encodes both content and temporal information about an item, event, or token in a single embedding. It is not a universally standardized concept, but rather a descriptor used in niche literature and practice to capture time-aware similarity.

Construction and composition: A totsvector typically combines three components: a content embedding that represents intrinsic attributes, a temporal encoding that reflects time or order, and an optional context or meta-embedding that encodes auxiliary factors such as user or environmental information. The components are concatenated and often projected down to a common dimensionality through a learned linear layer or quantization method. Temporal information can be encoded using sinusoidal functions, learned encodings, or hybrid schemes, and normalization (such as unit-length vectors) is common to facilitate cosine-based similarity.

Variants and properties: Different implementations vary in how they allocate dimensions among content, time, and context, and in how they weight these components during training. Some approaches emphasize temporal locality, making vectors for items with nearby timestamps more similar, while others focus on content fidelity. Proponents cite the ability to perform unified similarity search and indexing for time-stamped data.

Applications: Totsvectors are discussed in the context of sequence modeling, recommendation and ranking systems, and vector databases that require time-aware retrieval. They aim to support efficient similarity queries that respect both what a item is and when it occurred.

History and status: The term appears in limited technical discussions and blogs; no canonical standard exists, and implementations vary across domains.